src/codebase/iPython/Completeness_dataset/Awa2_VIT.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "f3ab4da5",
   "metadata": {},
   "outputs": [
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     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\\n\\nimport os\\nimport sys\\n\\nsys.path.append(\\n    os.path.abspath(\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/codebase\\\")\\n)\\nimport torch.backends.cudnn as cudnn\\nimport random\\nimport time\\nfrom collections import OrderedDict\\nimport pickle\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nimport torch.nn.functional as F\\nfrom torch.utils.data import DataLoader\\nfrom tqdm import tqdm\\nfrom torch.nn.functional import one_hot\\nimport sklearn.metrics as metrics\\nimport utils\\nfrom Explainer.loss_F import loss_fn_kd, entropy_loss\\nfrom Explainer.models.Gated_Logic_Net import Gated_Logic_Net\\nfrom Explainer.models.explainer import Explainer\\nfrom Explainer.models.pi import Pi\\nfrom dataset.dataset_cubs import Dataset_cub_for_explainer\\nfrom dataset.utils_dataset import get_dataset_with_image_and_attributes\\nfrom Explainer.loss_F import loss_fn_kd, entropy_loss, Selective_Distillation_Loss\\nfrom Explainer.models.concepts import Conceptizator\\nimport Plots.plot_for_paper as plot\\nimport matplotlib.pyplot as plt\";\n",
       "                var nbb_formatted_code = \"%load_ext nb_black\\n\\nimport os\\nimport sys\\n\\nsys.path.append(\\n    os.path.abspath(\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/codebase\\\")\\n)\\nimport torch.backends.cudnn as cudnn\\nimport random\\nimport time\\nfrom collections import OrderedDict\\nimport pickle\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nimport torch.nn.functional as F\\nfrom torch.utils.data import DataLoader\\nfrom tqdm import tqdm\\nfrom torch.nn.functional import one_hot\\nimport sklearn.metrics as metrics\\nimport utils\\nfrom Explainer.loss_F import loss_fn_kd, entropy_loss\\nfrom Explainer.models.Gated_Logic_Net import Gated_Logic_Net\\nfrom Explainer.models.explainer import Explainer\\nfrom Explainer.models.pi import Pi\\nfrom dataset.dataset_cubs import Dataset_cub_for_explainer\\nfrom dataset.utils_dataset import get_dataset_with_image_and_attributes\\nfrom Explainer.loss_F import loss_fn_kd, entropy_loss, Selective_Distillation_Loss\\nfrom Explainer.models.concepts import Conceptizator\\nimport Plots.plot_for_paper as plot\\nimport matplotlib.pyplot as plt\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                }\n",
       "            }, 500);\n",
       "            "
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   ],
   "source": [
    "%load_ext nb_black\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(\n",
    "    os.path.abspath(\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/codebase\")\n",
    ")\n",
    "import torch.backends.cudnn as cudnn\n",
    "import random\n",
    "import time\n",
    "from collections import OrderedDict\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "from torch.nn.functional import one_hot\n",
    "import sklearn.metrics as metrics\n",
    "import utils\n",
    "from Explainer.loss_F import loss_fn_kd, entropy_loss\n",
    "from Explainer.models.Gated_Logic_Net import Gated_Logic_Net\n",
    "from Explainer.models.explainer import Explainer\n",
    "from Explainer.models.pi import Pi\n",
    "from dataset.dataset_cubs import Dataset_cub_for_explainer\n",
    "from dataset.utils_dataset import get_dataset_with_image_and_attributes\n",
    "from Explainer.loss_F import loss_fn_kd, entropy_loss, Selective_Distillation_Loss\n",
    "from Explainer.models.concepts import Conceptizator\n",
    "import Plots.plot_for_paper as plot\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e888238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################\n",
      "[0.01]\n",
      "[0.2]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"lr = 0.01\\ncov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"iter1\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr)\\nprint(args.cov)\";\n",
       "                var nbb_formatted_code = \"lr = 0.01\\ncov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"iter1\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr)\\nprint(args.cov)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = 0.01\n",
    "cov = 0.2\n",
    "pickle_in = open(\n",
    "    os.path.join(\n",
    "        f\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\",\n",
    "        \"iter1\",\n",
    "        \"explainer\",\n",
    "        \"test_explainer_configs.pkl\",\n",
    "    ),\n",
    "    \"rb\",\n",
    ")\n",
    "args = pickle.load(pickle_in)\n",
    "n_classes = len(args.labels)\n",
    "x_to_bool = 0.5\n",
    "top_k_explanations = 50\n",
    "concept_names = args.concept_names\n",
    "print(\"########################\")\n",
    "print(args.lr)\n",
    "print(args.cov)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca08890a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_0.01_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_0.2_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none/iter1/explainer/g_outputs\n",
      "\n",
      "\n",
      " << Train sizes >>\n",
      "torch.Size([5739, 3, 448, 448])\n",
      "torch.Size([5739])\n",
      "torch.Size([5739, 85])\n",
      "\n",
      "\n",
      " << Test sizes >>\n",
      "torch.Size([1436, 3, 448, 448])\n",
      "torch.Size([1436])\n",
      "torch.Size([1436, 85])\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"use_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter1\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\nprint(os.path.join(root, experiment, iteration, expert_type, output))\\n\\ntrain_tensor_image_1 = torch.load(\\n    os.path.join(\\n        root, experiment, iteration, expert_type, output, \\\"train_tensor_images.pt\\\"\\n    )\\n)\\n\\ntrain_tensor_y_1 = torch.load(\\n    os.path.join(\\n        root, experiment, iteration, expert_type, output, \\\"train_tensor_y.pt\\\"\\n    )\\n)\\n\\ntrain_concept_mask_1 = torch.load(\\n    os.path.join(root, experiment, iteration, expert_type, output, \\\"train_mask_alpha.pt\\\")\\n)\\n\\ntest_tensor_image_1 = torch.load(\\n    os.path.join(\\n        root, experiment, iteration, expert_type, output, \\\"test_tensor_images.pt\\\"\\n    )\\n)\\n\\ntest_tensor_y_1 = torch.load(\\n    os.path.join(\\n        root, experiment, iteration, expert_type, output, \\\"test_tensor_y.pt\\\"\\n    )\\n)\\n\\ntest_concept_mask_1 = torch.load(\\n    os.path.join(root, experiment, iteration, expert_type, output, \\\"test_mask_alpha.pt\\\")\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_image_1.size())\\nprint(train_tensor_y_1.size())\\nprint(train_concept_mask_1.size())\\n\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_image_1.size())\\nprint(test_tensor_y_1.size())\\nprint(test_concept_mask_1.size())\";\n",
       "                var nbb_formatted_code = \"use_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter1\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\nprint(os.path.join(root, experiment, iteration, expert_type, output))\\n\\ntrain_tensor_image_1 = torch.load(\\n    os.path.join(\\n        root, experiment, iteration, expert_type, output, \\\"train_tensor_images.pt\\\"\\n    )\\n)\\n\\ntrain_tensor_y_1 = torch.load(\\n    os.path.join(root, experiment, iteration, expert_type, output, \\\"train_tensor_y.pt\\\")\\n)\\n\\ntrain_concept_mask_1 = torch.load(\\n    os.path.join(\\n        root, experiment, iteration, expert_type, output, \\\"train_mask_alpha.pt\\\"\\n    )\\n)\\n\\ntest_tensor_image_1 = torch.load(\\n    os.path.join(\\n        root, experiment, iteration, expert_type, output, \\\"test_tensor_images.pt\\\"\\n    )\\n)\\n\\ntest_tensor_y_1 = torch.load(\\n    os.path.join(root, experiment, iteration, expert_type, output, \\\"test_tensor_y.pt\\\")\\n)\\n\\ntest_concept_mask_1 = torch.load(\\n    os.path.join(root, experiment, iteration, expert_type, output, \\\"test_mask_alpha.pt\\\")\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_image_1.size())\\nprint(train_tensor_y_1.size())\\nprint(train_concept_mask_1.size())\\n\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_image_1.size())\\nprint(test_tensor_y_1.size())\\nprint(test_concept_mask_1.size())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
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       "                    }\n",
       "                }\n",
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       "            "
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    }
   ],
   "source": [
    "use_concepts_as_pi_input = True\n",
    "explainer_init = \"none\"\n",
    "\n",
    "experiment_folder = (\n",
    "    f\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\"\n",
    "    f\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\"\n",
    "    f\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\"\n",
    "    f\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\"\n",
    "    f\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\"\n",
    "    f\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\"\n",
    ")\n",
    "\n",
    "root = \"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\"\n",
    "experiment = f\"explainer/ViT-B_16/{experiment_folder}\"\n",
    "iteration = \"iter1\"\n",
    "expert_type = \"explainer\"\n",
    "output = \"g_outputs\"\n",
    "\n",
    "\n",
    "print(os.path.join(root, experiment, iteration, expert_type, output))\n",
    "\n",
    "train_tensor_image_1 = torch.load(\n",
    "    os.path.join(\n",
    "        root, experiment, iteration, expert_type, output, \"train_tensor_images.pt\"\n",
    "    )\n",
    ")\n",
    "\n",
    "train_tensor_y_1 = torch.load(\n",
    "    os.path.join(\n",
    "        root, experiment, iteration, expert_type, output, \"train_tensor_y.pt\"\n",
    "    )\n",
    ")\n",
    "\n",
    "train_concept_mask_1 = torch.load(\n",
    "    os.path.join(root, experiment, iteration, expert_type, output, \"train_mask_alpha.pt\")\n",
    ")\n",
    "\n",
    "test_tensor_image_1 = torch.load(\n",
    "    os.path.join(\n",
    "        root, experiment, iteration, expert_type, output, \"test_tensor_images.pt\"\n",
    "    )\n",
    ")\n",
    "\n",
    "test_tensor_y_1 = torch.load(\n",
    "    os.path.join(\n",
    "        root, experiment, iteration, expert_type, output, \"test_tensor_y.pt\"\n",
    "    )\n",
    ")\n",
    "\n",
    "test_concept_mask_1 = torch.load(\n",
    "    os.path.join(root, experiment, iteration, expert_type, output, \"test_mask_alpha.pt\")\n",
    ")\n",
    "\n",
    "\n",
    "print(\"\\n\\n << Train sizes >>\")\n",
    "print(train_tensor_image_1.size())\n",
    "print(train_tensor_y_1.size())\n",
    "print(train_concept_mask_1.size())\n",
    "\n",
    "\n",
    "print(\"\\n\\n << Test sizes >>\")\n",
    "print(test_tensor_image_1.size())\n",
    "print(test_tensor_y_1.size())\n",
    "print(test_concept_mask_1.size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6df28efa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################\n",
      "0.01\n",
      "0.2\n",
      "\n",
      "\n",
      " << Train sizes >>\n",
      "torch.Size([6548, 3, 448, 448])\n",
      "torch.Size([6548])\n",
      "torch.Size([6548, 85])\n",
      "\n",
      "\n",
      " << Test sizes >>\n",
      "torch.Size([1643, 3, 448, 448])\n",
      "torch.Size([1643])\n",
      "torch.Size([1643, 85])\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter2\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter2\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_2.size())\\nprint(train_tensor_y_2.size())\\nprint(train_mask_alpha_2.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_2.size())\\nprint(test_tensor_y_2.size())\\nprint(test_mask_alpha_2.size())\";\n",
       "                var nbb_formatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter2\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter2\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_2 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_2.size())\\nprint(train_tensor_y_2.size())\\nprint(train_mask_alpha_2.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_2.size())\\nprint(test_tensor_y_2.size())\\nprint(test_mask_alpha_2.size())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.01\n",
    "base_cov = 0.2\n",
    "pickle_in = open(\n",
    "    os.path.join(\n",
    "        f\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\",\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        \"iter2\",\n",
    "        \"explainer\",\n",
    "        \"test_explainer_configs.pkl\",\n",
    "    ),\n",
    "    \"rb\",\n",
    ")\n",
    "args = pickle.load(pickle_in)\n",
    "n_classes = len(args.labels)\n",
    "x_to_bool = 0.5\n",
    "top_k_explanations = 50\n",
    "concept_names = args.concept_names\n",
    "print(\"########################\")\n",
    "print(args.lr[-1])\n",
    "print(args.cov[-1])\n",
    "\n",
    "use_concepts_as_pi_input = True\n",
    "explainer_init = \"none\"\n",
    "\n",
    "experiment_folder = (\n",
    "    f\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\"\n",
    "    f\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\"\n",
    "    f\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\"\n",
    "    f\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\"\n",
    "    f\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\"\n",
    "    f\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\"\n",
    ")\n",
    "\n",
    "root = \"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\"\n",
    "experiment = f\"explainer/ViT-B_16/{experiment_folder}\"\n",
    "iteration = \"iter2\"\n",
    "expert_type = \"explainer\"\n",
    "output = \"g_outputs\"\n",
    "\n",
    "\n",
    "train_tensor_images_2 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_tensor_y_2 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_mask_alpha_2 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "test_tensor_images_2 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_tensor_y_2 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_mask_alpha_2 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "print(\"\\n\\n << Train sizes >>\")\n",
    "print(train_tensor_images_2.size())\n",
    "print(train_tensor_y_2.size())\n",
    "print(train_mask_alpha_2.size())\n",
    "\n",
    "print(\"\\n\\n << Test sizes >>\")\n",
    "print(test_tensor_images_2.size())\n",
    "print(test_tensor_y_2.size())\n",
    "print(test_mask_alpha_2.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6850212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################\n",
      "0.01\n",
      "0.2\n",
      "\n",
      "\n",
      " << Train sizes >>\n",
      "torch.Size([5707, 3, 448, 448])\n",
      "torch.Size([5707])\n",
      "torch.Size([5707, 85])\n",
      "\n",
      "\n",
      " << Test sizes >>\n",
      "torch.Size([1432, 3, 448, 448])\n",
      "torch.Size([1432])\n",
      "torch.Size([1432, 85])\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter3\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter3\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n       \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_3.size())\\nprint(train_tensor_y_3.size())\\nprint(train_mask_alpha_3.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_3.size())\\nprint(test_tensor_y_3.size())\\nprint(test_mask_alpha_3.size())\";\n",
       "                var nbb_formatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter3\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter3\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_3 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_3.size())\\nprint(train_tensor_y_3.size())\\nprint(train_mask_alpha_3.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_3.size())\\nprint(test_tensor_y_3.size())\\nprint(test_mask_alpha_3.size())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.01\n",
    "base_cov = 0.2\n",
    "pickle_in = open(\n",
    "    os.path.join(\n",
    "        f\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\",\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        \"iter3\",\n",
    "        \"explainer\",\n",
    "        \"test_explainer_configs.pkl\",\n",
    "    ),\n",
    "    \"rb\",\n",
    ")\n",
    "args = pickle.load(pickle_in)\n",
    "n_classes = len(args.labels)\n",
    "x_to_bool = 0.5\n",
    "top_k_explanations = 50\n",
    "concept_names = args.concept_names\n",
    "print(\"########################\")\n",
    "print(args.lr[-1])\n",
    "print(args.cov[-1])\n",
    "\n",
    "use_concepts_as_pi_input = True\n",
    "explainer_init = \"none\"\n",
    "\n",
    "experiment_folder = (\n",
    "    f\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\"\n",
    "    f\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\"\n",
    "    f\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\"\n",
    "    f\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\"\n",
    "    f\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\"\n",
    "    f\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\"\n",
    ")\n",
    "\n",
    "root = \"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\"\n",
    "experiment = f\"explainer/ViT-B_16/{experiment_folder}\"\n",
    "iteration = \"iter3\"\n",
    "expert_type = \"explainer\"\n",
    "output = \"g_outputs\"\n",
    "\n",
    "\n",
    "train_tensor_images_3 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_tensor_y_3 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_mask_alpha_3 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "test_tensor_images_3 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_tensor_y_3 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_mask_alpha_3 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "print(\"\\n\\n << Train sizes >>\")\n",
    "print(train_tensor_images_3.size())\n",
    "print(train_tensor_y_3.size())\n",
    "print(train_mask_alpha_3.size())\n",
    "\n",
    "print(\"\\n\\n << Test sizes >>\")\n",
    "print(test_tensor_images_3.size())\n",
    "print(test_tensor_y_3.size())\n",
    "print(test_mask_alpha_3.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bdd179b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################\n",
      "0.01\n",
      "0.2\n",
      "\n",
      "\n",
      " << Train sizes >>\n",
      "torch.Size([4002, 3, 448, 448])\n",
      "torch.Size([4002])\n",
      "torch.Size([4002, 85])\n",
      "\n",
      "\n",
      " << Test sizes >>\n",
      "torch.Size([1002, 3, 448, 448])\n",
      "torch.Size([1002])\n",
      "torch.Size([1002, 85])\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter4\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter4\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n       \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_4.size())\\nprint(train_tensor_y_4.size())\\nprint(train_mask_alpha_4.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_4.size())\\nprint(test_tensor_y_4.size())\\nprint(test_mask_alpha_4.size())\";\n",
       "                var nbb_formatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter4\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter4\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_4 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_4.size())\\nprint(train_tensor_y_4.size())\\nprint(train_mask_alpha_4.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_4.size())\\nprint(test_tensor_y_4.size())\\nprint(test_mask_alpha_4.size())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.01\n",
    "base_cov = 0.2\n",
    "pickle_in = open(\n",
    "    os.path.join(\n",
    "        f\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\",\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        \"iter4\",\n",
    "        \"explainer\",\n",
    "        \"test_explainer_configs.pkl\",\n",
    "    ),\n",
    "    \"rb\",\n",
    ")\n",
    "args = pickle.load(pickle_in)\n",
    "n_classes = len(args.labels)\n",
    "x_to_bool = 0.5\n",
    "top_k_explanations = 50\n",
    "concept_names = args.concept_names\n",
    "print(\"########################\")\n",
    "print(args.lr[-1])\n",
    "print(args.cov[-1])\n",
    "\n",
    "use_concepts_as_pi_input = True\n",
    "explainer_init = \"none\"\n",
    "\n",
    "experiment_folder = (\n",
    "    f\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\"\n",
    "    f\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\"\n",
    "    f\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\"\n",
    "    f\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\"\n",
    "    f\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\"\n",
    "    f\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\"\n",
    ")\n",
    "\n",
    "root = \"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\"\n",
    "experiment = f\"explainer/ViT-B_16/{experiment_folder}\"\n",
    "iteration = \"iter4\"\n",
    "expert_type = \"explainer\"\n",
    "output = \"g_outputs\"\n",
    "\n",
    "\n",
    "train_tensor_images_4 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_tensor_y_4 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_mask_alpha_4 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "test_tensor_images_4 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_tensor_y_4 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_mask_alpha_4 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "print(\"\\n\\n << Train sizes >>\")\n",
    "print(train_tensor_images_4.size())\n",
    "print(train_tensor_y_4.size())\n",
    "print(train_mask_alpha_4.size())\n",
    "\n",
    "print(\"\\n\\n << Test sizes >>\")\n",
    "print(test_tensor_images_4.size())\n",
    "print(test_tensor_y_4.size())\n",
    "print(test_mask_alpha_4.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2fdfa50a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################\n",
      "0.01\n",
      "0.2\n",
      "\n",
      "\n",
      " << Train sizes >>\n",
      "torch.Size([3636, 3, 448, 448])\n",
      "torch.Size([3636])\n",
      "torch.Size([3636, 85])\n",
      "\n",
      "\n",
      " << Test sizes >>\n",
      "torch.Size([918, 3, 448, 448])\n",
      "torch.Size([918])\n",
      "torch.Size([918, 85])\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter5\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter5\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n       \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_5.size())\\nprint(train_tensor_y_5.size())\\nprint(train_mask_alpha_5.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_5.size())\\nprint(test_tensor_y_5.size())\\nprint(test_mask_alpha_5.size())\";\n",
       "                var nbb_formatted_code = \"base_lr = 0.01\\nbase_cov = 0.2\\npickle_in = open(\\n    os.path.join(\\n        f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\\\",\\n        \\\"cov_0.2_lr_0.01\\\",\\n        \\\"iter5\\\",\\n        \\\"explainer\\\",\\n        \\\"test_explainer_configs.pkl\\\",\\n    ),\\n    \\\"rb\\\",\\n)\\nargs = pickle.load(pickle_in)\\nn_classes = len(args.labels)\\nx_to_bool = 0.5\\ntop_k_explanations = 50\\nconcept_names = args.concept_names\\nprint(\\\"########################\\\")\\nprint(args.lr[-1])\\nprint(args.cov[-1])\\n\\nuse_concepts_as_pi_input = True\\nexplainer_init = \\\"none\\\"\\n\\nexperiment_folder = (\\n    f\\\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\\\"\\n    f\\\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\\\"\\n    f\\\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\\\"\\n    f\\\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\\\"\\n    f\\\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\\\"\\n    f\\\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\\\"\\n)\\n\\nroot = \\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\\\"\\nexperiment = f\\\"explainer/ViT-B_16/{experiment_folder}\\\"\\niteration = \\\"iter5\\\"\\nexpert_type = \\\"explainer\\\"\\noutput = \\\"g_outputs\\\"\\n\\n\\ntrain_tensor_images_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_images.pt\\\",\\n    )\\n)\\n\\ntrain_tensor_y_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_tensor_y.pt\\\",\\n    )\\n)\\n\\ntrain_mask_alpha_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"train_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\ntest_tensor_images_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_images.pt\\\",\\n    )\\n)\\n\\ntest_tensor_y_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_tensor_y.pt\\\",\\n    )\\n)\\n\\ntest_mask_alpha_5 = torch.load(\\n    os.path.join(\\n        root,\\n        experiment,\\n        \\\"cov_0.2_lr_0.01\\\",\\n        iteration,\\n        expert_type,\\n        output,\\n        \\\"test_mask_alpha.pt\\\",\\n    )\\n)\\n\\n\\nprint(\\\"\\\\n\\\\n << Train sizes >>\\\")\\nprint(train_tensor_images_5.size())\\nprint(train_tensor_y_5.size())\\nprint(train_mask_alpha_5.size())\\n\\nprint(\\\"\\\\n\\\\n << Test sizes >>\\\")\\nprint(test_tensor_images_5.size())\\nprint(test_tensor_y_5.size())\\nprint(test_mask_alpha_5.size())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.01\n",
    "base_cov = 0.2\n",
    "pickle_in = open(\n",
    "    os.path.join(\n",
    "        f\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/explainer/ViT-B_16/lr_{base_lr}_epochs_500_temperature-lens_6.0_use-concepts-as-pi-input_True_input-size-pi_2048_cov_{base_cov}_alpha_0.5_selection-threshold_0.5_lambda-lens_0.0001_alpha-KD_0.99_temperature-KD_10.0_hidden-layers_1_layer_VIT_explainer_init_none\",\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        \"iter5\",\n",
    "        \"explainer\",\n",
    "        \"test_explainer_configs.pkl\",\n",
    "    ),\n",
    "    \"rb\",\n",
    ")\n",
    "args = pickle.load(pickle_in)\n",
    "n_classes = len(args.labels)\n",
    "x_to_bool = 0.5\n",
    "top_k_explanations = 50\n",
    "concept_names = args.concept_names\n",
    "print(\"########################\")\n",
    "print(args.lr[-1])\n",
    "print(args.cov[-1])\n",
    "\n",
    "use_concepts_as_pi_input = True\n",
    "explainer_init = \"none\"\n",
    "\n",
    "experiment_folder = (\n",
    "    f\"lr_{args.lr[0]}_epochs_{args.epochs}_temperature-lens_{args.temperature_lens}\"\n",
    "    f\"_use-concepts-as-pi-input_{use_concepts_as_pi_input}_input-size-pi_{args.input_size_pi}\"\n",
    "    f\"_cov_{args.cov[0]}_alpha_{args.alpha}_selection-threshold_{args.selection_threshold}\"\n",
    "    f\"_lambda-lens_{args.lambda_lens}_alpha-KD_{args.alpha_KD}\"\n",
    "    f\"_temperature-KD_{float(args.temperature_KD)}_hidden-layers_{len(args.hidden_nodes)}\"\n",
    "    f\"_layer_{args.layer}_explainer_init_{explainer_init if not args.explainer_init else args.explainer_init}\"\n",
    ")\n",
    "\n",
    "root = \"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/\"\n",
    "experiment = f\"explainer/ViT-B_16/{experiment_folder}\"\n",
    "iteration = \"iter5\"\n",
    "expert_type = \"explainer\"\n",
    "output = \"g_outputs\"\n",
    "\n",
    "\n",
    "train_tensor_images_5 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_tensor_y_5 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "train_mask_alpha_5 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"train_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "test_tensor_images_5 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_images.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_tensor_y_5 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_tensor_y.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "test_mask_alpha_5 = torch.load(\n",
    "    os.path.join(\n",
    "        root,\n",
    "        experiment,\n",
    "        \"cov_0.2_lr_0.01\",\n",
    "        iteration,\n",
    "        expert_type,\n",
    "        output,\n",
    "        \"test_mask_alpha.pt\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "print(\"\\n\\n << Train sizes >>\")\n",
    "print(train_tensor_images_5.size())\n",
    "print(train_tensor_y_5.size())\n",
    "print(train_mask_alpha_5.size())\n",
    "\n",
    "print(\"\\n\\n << Test sizes >>\")\n",
    "print(test_tensor_images_5.size())\n",
    "print(test_tensor_y_5.size())\n",
    "print(test_mask_alpha_5.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7da20967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([25632, 3, 448, 448])\n",
      "torch.Size([25632])\n",
      "torch.Size([25632, 85])\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"train_tensor_images = torch.cat(\\n    (train_tensor_image_1, train_tensor_images_2, train_tensor_images_3, train_tensor_images_4, \\n     train_tensor_images_5), dim=0\\n)\\ntrain_tensor_y = torch.cat(\\n    (train_tensor_y_1, train_tensor_y_2, train_tensor_y_3, train_tensor_y_4,\\n    train_tensor_y_5), dim=0\\n)\\ntrain_mask_alpha = torch.cat(\\n    (train_concept_mask_1, train_mask_alpha_2, train_mask_alpha_3, train_mask_alpha_4, train_mask_alpha_5), dim=0\\n)\\n\\nprint(train_tensor_images.size())\\nprint(train_tensor_y.size())\\nprint(train_mask_alpha.size())\";\n",
       "                var nbb_formatted_code = \"train_tensor_images = torch.cat(\\n    (\\n        train_tensor_image_1,\\n        train_tensor_images_2,\\n        train_tensor_images_3,\\n        train_tensor_images_4,\\n        train_tensor_images_5,\\n    ),\\n    dim=0,\\n)\\ntrain_tensor_y = torch.cat(\\n    (\\n        train_tensor_y_1,\\n        train_tensor_y_2,\\n        train_tensor_y_3,\\n        train_tensor_y_4,\\n        train_tensor_y_5,\\n    ),\\n    dim=0,\\n)\\ntrain_mask_alpha = torch.cat(\\n    (\\n        train_concept_mask_1,\\n        train_mask_alpha_2,\\n        train_mask_alpha_3,\\n        train_mask_alpha_4,\\n        train_mask_alpha_5,\\n    ),\\n    dim=0,\\n)\\n\\nprint(train_tensor_images.size())\\nprint(train_tensor_y.size())\\nprint(train_mask_alpha.size())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_tensor_images = torch.cat(\n",
    "    (train_tensor_image_1, train_tensor_images_2, train_tensor_images_3, train_tensor_images_4, \n",
    "     train_tensor_images_5), dim=0\n",
    ")\n",
    "train_tensor_y = torch.cat(\n",
    "    (train_tensor_y_1, train_tensor_y_2, train_tensor_y_3, train_tensor_y_4,\n",
    "    train_tensor_y_5), dim=0\n",
    ")\n",
    "train_mask_alpha = torch.cat(\n",
    "    (train_concept_mask_1, train_mask_alpha_2, train_mask_alpha_3, train_mask_alpha_4, train_mask_alpha_5), dim=0\n",
    ")\n",
    "\n",
    "print(train_tensor_images.size())\n",
    "print(train_tensor_y.size())\n",
    "print(train_mask_alpha.size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fe3d2426",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([6431, 3, 448, 448])\n",
      "torch.Size([6431])\n",
      "torch.Size([6431, 85])\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"test_tensor_images = torch.cat(\\n    (test_tensor_image_1, test_tensor_images_2, test_tensor_images_3, test_tensor_images_4,\\n    test_tensor_images_5), dim=0\\n)\\ntest_tensor_y = torch.cat(\\n    (test_tensor_y_1, test_tensor_y_2, test_tensor_y_3, test_tensor_y_4, test_tensor_y_5), dim=0\\n)\\ntest_mask_alpha = torch.cat(\\n    (test_concept_mask_1, test_mask_alpha_2, test_mask_alpha_3, test_mask_alpha_4, test_mask_alpha_5), dim=0\\n)\\n\\nprint(test_tensor_images.size())\\nprint(test_tensor_y.size())\\nprint(test_mask_alpha.size())\";\n",
       "                var nbb_formatted_code = \"test_tensor_images = torch.cat(\\n    (\\n        test_tensor_image_1,\\n        test_tensor_images_2,\\n        test_tensor_images_3,\\n        test_tensor_images_4,\\n        test_tensor_images_5,\\n    ),\\n    dim=0,\\n)\\ntest_tensor_y = torch.cat(\\n    (\\n        test_tensor_y_1,\\n        test_tensor_y_2,\\n        test_tensor_y_3,\\n        test_tensor_y_4,\\n        test_tensor_y_5,\\n    ),\\n    dim=0,\\n)\\ntest_mask_alpha = torch.cat(\\n    (\\n        test_concept_mask_1,\\n        test_mask_alpha_2,\\n        test_mask_alpha_3,\\n        test_mask_alpha_4,\\n        test_mask_alpha_5,\\n    ),\\n    dim=0,\\n)\\n\\nprint(test_tensor_images.size())\\nprint(test_tensor_y.size())\\nprint(test_mask_alpha.size())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_tensor_images = torch.cat(\n",
    "    (test_tensor_image_1, test_tensor_images_2, test_tensor_images_3, test_tensor_images_4,\n",
    "    test_tensor_images_5), dim=0\n",
    ")\n",
    "test_tensor_y = torch.cat(\n",
    "    (test_tensor_y_1, test_tensor_y_2, test_tensor_y_3, test_tensor_y_4, test_tensor_y_5), dim=0\n",
    ")\n",
    "test_mask_alpha = torch.cat(\n",
    "    (test_concept_mask_1, test_mask_alpha_2, test_mask_alpha_3, test_mask_alpha_4, test_mask_alpha_5), dim=0\n",
    ")\n",
    "\n",
    "print(test_tensor_images.size())\n",
    "print(test_tensor_y.size())\n",
    "print(test_mask_alpha.size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b5ec25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"save_path = f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/{args.dataset}/completeness/{args.arch}/dataset\\\"\\nos.makedirs(save_path, exist_ok=True)\\ntorch.save(train_tensor_images, os.path.join(save_path, f\\\"train_tensor_images.pt\\\"))\\ntorch.save(train_tensor_y, os.path.join(save_path, f\\\"train_tensor_y.pt\\\"))\\ntorch.save(train_mask_alpha, os.path.join(save_path, f\\\"train_mask_alpha.pt\\\"))\\n\\ntorch.save(test_tensor_images, os.path.join(save_path, f\\\"test_tensor_images.pt\\\"))\\ntorch.save(test_tensor_y, os.path.join(save_path, f\\\"test_tensor_y.pt\\\"))\\ntorch.save(test_mask_alpha, os.path.join(save_path, f\\\"test_mask_alpha.pt\\\"))\";\n",
       "                var nbb_formatted_code = \"save_path = f\\\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/{args.dataset}/completeness/{args.arch}/dataset\\\"\\nos.makedirs(save_path, exist_ok=True)\\ntorch.save(train_tensor_images, os.path.join(save_path, f\\\"train_tensor_images.pt\\\"))\\ntorch.save(train_tensor_y, os.path.join(save_path, f\\\"train_tensor_y.pt\\\"))\\ntorch.save(train_mask_alpha, os.path.join(save_path, f\\\"train_mask_alpha.pt\\\"))\\n\\ntorch.save(test_tensor_images, os.path.join(save_path, f\\\"test_tensor_images.pt\\\"))\\ntorch.save(test_tensor_y, os.path.join(save_path, f\\\"test_tensor_y.pt\\\"))\\ntorch.save(test_mask_alpha, os.path.join(save_path, f\\\"test_mask_alpha.pt\\\"))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_path = f\"/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/{args.dataset}/completeness/{args.arch}/dataset\"\n",
    "os.makedirs(save_path, exist_ok=True)\n",
    "print(save_path)\n",
    "torch.save(train_tensor_images, os.path.join(save_path, f\"train_tensor_images.pt\"))\n",
    "torch.save(train_tensor_y, os.path.join(save_path, f\"train_tensor_y.pt\"))\n",
    "torch.save(train_mask_alpha, os.path.join(save_path, f\"train_mask_alpha.pt\"))\n",
    "\n",
    "torch.save(test_tensor_images, os.path.join(save_path, f\"test_tensor_images.pt\"))\n",
    "torch.save(test_tensor_y, os.path.join(save_path, f\"test_tensor_y.pt\"))\n",
    "torch.save(test_mask_alpha, os.path.join(save_path, f\"test_mask_alpha.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f96738b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ocean/projects/asc170022p/shg121/PhD/ICLR-2022/out/awa2/completeness/ViT-B_16/dataset\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"print(save_path)\";\n",
       "                var nbb_formatted_code = \"print(save_path)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(save_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python_3_7",
   "language": "python",
   "name": "python_3_7"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
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