Food_Vision_model_deployment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/Azamat0315277/pytorch_model_deployment/blob/main/Food_Vision_model_deployment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X35rj4tvjc6q"
   },
   "source": [
    "# Getting setup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QMKV5lxMlOpB",
    "outputId": "1103fd2e-1930-4647-98f2-42c6235fd7a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Couldn't find torchinfo... installing it.\n",
      "[INFO] Couldn't find going_modular or helper_functions scripts\n",
      "Cloning into 'pytorch_paper_replicating_for_computer_vision'...\n",
      "remote: Enumerating objects: 44, done.\u001b[K\n",
      "remote: Counting objects: 100% (44/44), done.\u001b[K\n",
      "remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
      "remote: Total 44 (delta 5), reused 0 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (44/44), 24.58 MiB | 4.34 MiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "# import scripts\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "from torchvision import transforms\n",
    "\n",
    "# Try to get torchinfo()\n",
    "try:\n",
    "  from torchinfo import summary\n",
    "except:\n",
    "  print(f\"[INFO] Couldn't find torchinfo... installing it.\")\n",
    "  !pip install -q torchinfo\n",
    "  from torchinfo import summary\n",
    "\n",
    "# Try to import the going_modular directory\n",
    "try:\n",
    "  from going_modular import data_setup, engine\n",
    "  from helper_functions import download_data, set_seeds, plot_loss_curves\n",
    "except:\n",
    "  # Get the going_modular scripts\n",
    "  print(f\"[INFO] Couldn't find going_modular or helper_functions scripts\")\n",
    "  #!git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
    "  !git clone https://github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision\n",
    "\n",
    "  !mv pytorch_paper_replicating_for_computer_vision/going_modular .\n",
    "  !mv pytorch_paper_replicating_for_computer_vision/helper_functions.py . \n",
    "  !rm -rf pytorch_paper_replicating_for_computer_vision\n",
    "  from going_modular import data_setup, engine\n",
    "  from helper_functions import download_data, set_seeds, plot_loss_curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "NBvhQpXIlY3i",
    "outputId": "50c0cf65-6168-432e-9b12-bfdd57e7ec24"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device_cuda = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "device_cuda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aenA5hoBlvDV"
   },
   "source": [
    "# Getting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7fXncTNNlxmk",
    "outputId": "f404cd30-e06a-44cb-95ae-344d04326601"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Did not find data/pizza_steak_sushi_20_percent directory, creating one...\n",
      "[INFO] Downloading pizza_steak_sushi_20_percent.zip from https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip...\n",
      "[INFO] Unzipping pizza_steak_sushi_20_percent.zip data...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('data/pizza_steak_sushi_20_percent')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download pizza, steak, sushi images from GitHub\n",
    "data_20_percent_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\",\n",
    "                                     destination=\"pizza_steak_sushi_20_percent\")\n",
    "\n",
    "data_20_percent_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8YgaM1EEvRdI"
   },
   "outputs": [],
   "source": [
    "# Setup directory paths\n",
    "train_dir = data_20_percent_path / 'train'\n",
    "test_dir = data_20_percent_path / 'test'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qa2YsyJbm_f-"
   },
   "source": [
    "# Food Vision model deployment deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mDSQW5VmnYf2"
   },
   "source": [
    "## Creating an EffNetB2 feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 65,
     "referenced_widgets": [
      "82232cfbfd924b948e6317c1ec567477",
      "07fcc51306f24ec6ad11d42e9c26158b",
      "56b3fdca67f24056a3f0671dfb154f51",
      "2f257d23b23f408d8cba39c81dad14a9",
      "7a6915aa66c34e62bfad8915eb3a4080",
      "25d88390bdc94374b073f0ef1965deff",
      "8c9bff84c57b468f8c0c2f7ede3bcf31",
      "b01af292bf584575b9b515bac6d0903a",
      "82ad31b7f1604eb385b91c37ea181fee",
      "32f79ce465f7493c94015aaf190e0526",
      "a05df77632f340699514f6d13103ad83"
     ]
    },
    "id": "tt0LN6sUnevQ",
    "outputId": "cad53b2d-c5ff-4d6c-b8db-448b5721c1f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b2_rwightman-bcdf34b7.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82232cfbfd924b948e6317c1ec567477",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/35.2M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup pretrained EffNetB2 weights\n",
    "effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "# Get EffNetB2 transforms\n",
    "effnetb2_transforms = effnetb2_weights.transforms()\n",
    "# Setup pratrained model\n",
    "effnet_b2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights)\n",
    "\n",
    "# freeze the base layers\n",
    "for param in effnet_b2.parameters():\n",
    "  param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SyC5ZHNDo0R9",
    "outputId": "5916029b-7fba-4eee-d0d4-f0e14dd6b8e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Dropout(p=0.3, inplace=True)\n",
       "  (1): Linear(in_features=1408, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check out model head\n",
    "effnet_b2.classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_1ZckWEJpAL3"
   },
   "outputs": [],
   "source": [
    "# Update (change) the classifer head\n",
    "effnet_b2.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.3, inplace=True),\n",
    "    nn.Linear(in_features=1408,\n",
    "              out_features=3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oiHCm8jzqu6q"
   },
   "source": [
    "## Creating a function to make an EffNetB2 feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "pXXGwYeUrjxX"
   },
   "outputs": [],
   "source": [
    "def create_effnetb2_model(num_classes: int=3,\n",
    "                          seed: int=42):\n",
    "  \"\"\"\n",
    "  Creates an EffNetB2 feature extractor model and transforms\n",
    "  \"\"\"\n",
    "\n",
    "  # Create EffNetB2 pretrained weights, transforms and model\n",
    "  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "  transforms = weights.transforms()\n",
    "  model = torchvision.models.efficientnet_b2(weights=weights)\n",
    "\n",
    "  # Freeze all layers in base model\n",
    "  for param in model.parameters():\n",
    "    requires_grad=False\n",
    "\n",
    "  # Change classifier head with random seed for reproducibility\n",
    "  torch.manual_seed(seed)\n",
    "  model.classifier = nn.Sequential(\n",
    "      nn.Dropout(p=0.3, inplace=True),\n",
    "      nn.Linear(in_features=1408,\n",
    "                out_features=num_classes)\n",
    "  )\n",
    "  return model, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oEqbeec1tkNk"
   },
   "outputs": [],
   "source": [
    "effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3,\n",
    "                                                      seed=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Un_rWzD_tv7l",
    "outputId": "26e554cc-3e27-4c11-a43f-db58d8841218"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "============================================================================================================================================\n",
       "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
       "============================================================================================================================================\n",
       "EfficientNet (EfficientNet)                                  [1, 3, 224, 224]     [1, 3]               --                   True\n",
       "├─Sequential (features)                                      [1, 3, 224, 224]     [1, 1408, 7, 7]      --                   True\n",
       "│    └─Conv2dNormActivation (0)                              [1, 3, 224, 224]     [1, 32, 112, 112]    --                   True\n",
       "│    │    └─Conv2d (0)                                       [1, 3, 224, 224]     [1, 32, 112, 112]    864                  True\n",
       "│    │    └─BatchNorm2d (1)                                  [1, 32, 112, 112]    [1, 32, 112, 112]    64                   True\n",
       "│    │    └─SiLU (2)                                         [1, 32, 112, 112]    [1, 32, 112, 112]    --                   --\n",
       "│    └─Sequential (1)                                        [1, 32, 112, 112]    [1, 16, 112, 112]    --                   True\n",
       "│    │    └─MBConv (0)                                       [1, 32, 112, 112]    [1, 16, 112, 112]    1,448                True\n",
       "│    │    └─MBConv (1)                                       [1, 16, 112, 112]    [1, 16, 112, 112]    612                  True\n",
       "│    └─Sequential (2)                                        [1, 16, 112, 112]    [1, 24, 56, 56]      --                   True\n",
       "│    │    └─MBConv (0)                                       [1, 16, 112, 112]    [1, 24, 56, 56]      6,004                True\n",
       "│    │    └─MBConv (1)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      10,710               True\n",
       "│    │    └─MBConv (2)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      10,710               True\n",
       "│    └─Sequential (3)                                        [1, 24, 56, 56]      [1, 48, 28, 28]      --                   True\n",
       "│    │    └─MBConv (0)                                       [1, 24, 56, 56]      [1, 48, 28, 28]      16,518               True\n",
       "│    │    └─MBConv (1)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      43,308               True\n",
       "│    │    └─MBConv (2)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      43,308               True\n",
       "│    └─Sequential (4)                                        [1, 48, 28, 28]      [1, 88, 14, 14]      --                   True\n",
       "│    │    └─MBConv (0)                                       [1, 48, 28, 28]      [1, 88, 14, 14]      50,300               True\n",
       "│    │    └─MBConv (1)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      123,750              True\n",
       "│    │    └─MBConv (2)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      123,750              True\n",
       "│    │    └─MBConv (3)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      123,750              True\n",
       "│    └─Sequential (5)                                        [1, 88, 14, 14]      [1, 120, 14, 14]     --                   True\n",
       "│    │    └─MBConv (0)                                       [1, 88, 14, 14]      [1, 120, 14, 14]     149,158              True\n",
       "│    │    └─MBConv (1)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     237,870              True\n",
       "│    │    └─MBConv (2)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     237,870              True\n",
       "│    │    └─MBConv (3)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     237,870              True\n",
       "│    └─Sequential (6)                                        [1, 120, 14, 14]     [1, 208, 7, 7]       --                   True\n",
       "│    │    └─MBConv (0)                                       [1, 120, 14, 14]     [1, 208, 7, 7]       301,406              True\n",
       "│    │    └─MBConv (1)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       686,868              True\n",
       "│    │    └─MBConv (2)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       686,868              True\n",
       "│    │    └─MBConv (3)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       686,868              True\n",
       "│    │    └─MBConv (4)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       686,868              True\n",
       "│    └─Sequential (7)                                        [1, 208, 7, 7]       [1, 352, 7, 7]       --                   True\n",
       "│    │    └─MBConv (0)                                       [1, 208, 7, 7]       [1, 352, 7, 7]       846,900              True\n",
       "│    │    └─MBConv (1)                                       [1, 352, 7, 7]       [1, 352, 7, 7]       1,888,920            True\n",
       "│    └─Conv2dNormActivation (8)                              [1, 352, 7, 7]       [1, 1408, 7, 7]      --                   True\n",
       "│    │    └─Conv2d (0)                                       [1, 352, 7, 7]       [1, 1408, 7, 7]      495,616              True\n",
       "│    │    └─BatchNorm2d (1)                                  [1, 1408, 7, 7]      [1, 1408, 7, 7]      2,816                True\n",
       "│    │    └─SiLU (2)                                         [1, 1408, 7, 7]      [1, 1408, 7, 7]      --                   --\n",
       "├─AdaptiveAvgPool2d (avgpool)                                [1, 1408, 7, 7]      [1, 1408, 1, 1]      --                   --\n",
       "├─Sequential (classifier)                                    [1, 1408]            [1, 3]               --                   True\n",
       "│    └─Dropout (0)                                           [1, 1408]            [1, 1408]            --                   --\n",
       "│    └─Linear (1)                                            [1, 1408]            [1, 3]               4,227                True\n",
       "============================================================================================================================================\n",
       "Total params: 7,705,221\n",
       "Trainable params: 7,705,221\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 657.64\n",
       "============================================================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 156.80\n",
       "Params size (MB): 30.82\n",
       "Estimated Total Size (MB): 188.22\n",
       "============================================================================================================================================"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary of model\n",
    "\n",
    "from torchinfo import summary \n",
    "# Print EffNetB2 model summary\n",
    "\n",
    "summary(effnetb2,\n",
    "        input_size=(1, 3, 224, 224),\n",
    "        col_names=['input_size', 'output_size', 'num_params', 'trainable'],\n",
    "        col_width=20,\n",
    "        row_settings=['var_names'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9jZ_CzX2uf3P"
   },
   "source": [
    "## Creating DataLoaders for EffNetB2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "a0Rd0VtsuvRA"
   },
   "outputs": [],
   "source": [
    "# Setup Dataloader\n",
    "from going_modular import data_setup \n",
    "\n",
    "train_dataloader_effnetb2, test_dataloader_effnetb2, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                                 test_dir=test_dir,\n",
    "                                                                                                 transform=effnetb2_transforms,\n",
    "                                                                                                 batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XhXTKLL6vqsm"
   },
   "source": [
    "## Training EffNetB2 feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378,
     "referenced_widgets": [
      "e0ff2912385146e294f22f0727145ae7",
      "864dddefe5594c51b38e9dcf31ce7c8a",
      "7ac09671ee354e2f94c5e737c96a7328",
      "84b13ae6858c46acac57fba8f06f2b72",
      "e09a4737b4504235a668238b1b1257af",
      "f761e37a08d148a9b346c942ac368041",
      "dffad845e7a3474095bc6bd50ac68966",
      "05f8e2eb04ee4302b9652d43fa1b3248",
      "99eccc0618f949caaf82058268bae425",
      "461343d7fecb4f22945e91c4cd9ddd91",
      "388dabafe3dd466c85f3fd892222fa5a"
     ]
    },
    "id": "mgUAlE3Hvwvj",
    "outputId": "d26f0fd8-c6ba-4c7a-dfb8-a30b04703622"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0ff2912385146e294f22f0727145ae7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.4683 | train_acc: 0.8250 | test_loss: 0.1230 | test_acc: 0.9722\n",
      "Epoch: 2 | train_loss: 0.2696 | train_acc: 0.9396 | test_loss: 0.1559 | test_acc: 0.9437\n",
      "Epoch: 3 | train_loss: 0.2660 | train_acc: 0.9271 | test_loss: 0.1126 | test_acc: 0.9625\n",
      "Epoch: 4 | train_loss: 0.1949 | train_acc: 0.9229 | test_loss: 0.1194 | test_acc: 0.9500\n",
      "Epoch: 5 | train_loss: 0.2322 | train_acc: 0.9354 | test_loss: 0.0629 | test_acc: 0.9875\n",
      "Epoch: 6 | train_loss: 0.2456 | train_acc: 0.9292 | test_loss: 0.1314 | test_acc: 0.9784\n",
      "Epoch: 7 | train_loss: 0.6145 | train_acc: 0.8729 | test_loss: 0.4380 | test_acc: 0.8682\n",
      "Epoch: 8 | train_loss: 0.4341 | train_acc: 0.8604 | test_loss: 0.1944 | test_acc: 0.9386\n",
      "Epoch: 9 | train_loss: 0.1903 | train_acc: 0.9250 | test_loss: 0.1027 | test_acc: 0.9597\n",
      "Epoch: 10 | train_loss: 0.1467 | train_acc: 0.9083 | test_loss: 0.0759 | test_acc: 0.9750\n",
      "Epoch: 11 | train_loss: 0.1606 | train_acc: 0.9479 | test_loss: 0.1561 | test_acc: 0.9375\n",
      "Epoch: 12 | train_loss: 0.1008 | train_acc: 0.9542 | test_loss: 0.1462 | test_acc: 0.9415\n",
      "Epoch: 13 | train_loss: 0.0845 | train_acc: 0.9583 | test_loss: 0.1402 | test_acc: 0.9443\n",
      "Epoch: 14 | train_loss: 0.0969 | train_acc: 0.9854 | test_loss: 0.1601 | test_acc: 0.9659\n",
      "Epoch: 15 | train_loss: 0.1110 | train_acc: 0.9688 | test_loss: 0.2313 | test_acc: 0.9261\n",
      "Epoch: 16 | train_loss: 0.2500 | train_acc: 0.9375 | test_loss: 0.2146 | test_acc: 0.9381\n",
      "Epoch: 17 | train_loss: 0.0572 | train_acc: 0.9875 | test_loss: 0.1955 | test_acc: 0.9261\n",
      "Epoch: 18 | train_loss: 0.0763 | train_acc: 0.9542 | test_loss: 0.1560 | test_acc: 0.9477\n",
      "Epoch: 19 | train_loss: 0.0352 | train_acc: 0.9958 | test_loss: 0.1547 | test_acc: 0.9449\n",
      "Epoch: 20 | train_loss: 0.1147 | train_acc: 0.9667 | test_loss: 0.1994 | test_acc: 0.9477\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine \n",
    "\n",
    "# Setup optimizer\n",
    "\n",
    "optimizer = torch.optim.Adam(params=effnetb2.parameters(),\n",
    "                             lr=0.001)\n",
    "# Setup loss\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# set seeds\n",
    "\n",
    "set_seeds()\n",
    "\n",
    "effnetb2_results = engine.train(model=effnetb2,\n",
    "                                train_dataloader=train_dataloader_effnetb2,\n",
    "                                test_dataloader=test_dataloader_effnetb2,\n",
    "                                epochs=20,\n",
    "                                optimizer=optimizer,\n",
    "                                loss_fn=loss_fn,\n",
    "                                device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OJWAdoVzw5m6"
   },
   "source": [
    "## Inspecting EffNetB2 loss curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "6Doszpghwl2n",
    "outputId": "205d99f6-54cc-433a-efee-242ceeb393ab"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(effnetb2_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-41wkBpxw0oB"
   },
   "source": [
    "## Saving EffNetB2 feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PmNsbkcOxN0v",
    "outputId": "cc99252a-789a-4f96-a807-701b4535c801"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n"
     ]
    }
   ],
   "source": [
    "from going_modular import utils\n",
    "\n",
    "# Save the model\n",
    "utils.save_model(model=effnetb2,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=\"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMxe1fgdxXKn"
   },
   "source": [
    "## Checking the size of EffNetB2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PATybv1hxe90",
    "outputId": "d6fc3d09-2e03-4849-d2bb-b7e949760ef2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrained EffNetB2 feature extractor model size: 29 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "pretrained_effnetb2_model_size = Path(\"models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\").stat().st_size //(1024*1024) # division converts bytes to megabytes \n",
    "\n",
    "print(f\"Pretrained EffNetB2 feature extractor model size: {pretrained_effnetb2_model_size} MB\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IMbatGB-yQP4"
   },
   "source": [
    "## Collecting EffNetB2 stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5oIVXNFcycN_",
    "outputId": "e9d8f89a-4f70-427f-cc99-9f82f8bd4f46"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7705221"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count nuumber of parameters in EffNetB2 \n",
    "effnetb2_total_params = sum(torch.numel(param) for param in effnetb2.parameters())\n",
    "effnetb2_total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W-FLu-DSyvL5",
    "outputId": "ca84b4d0-4fcb-4d1a-97a3-fcc97bae1bc2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.1993967060931027,\n",
       " 'test_acc': 0.9477272727272726,\n",
       " 'number_of_parameters': 7705221,\n",
       " 'model_size (MB)': 29}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary with EffNetB2 stats\n",
    "\n",
    "effnetb2_stats = {\"test_loss\": effnetb2_results['test_loss'][-1],\n",
    "                  \"test_acc\": effnetb2_results['test_acc'][-1],\n",
    "                  'number_of_parameters': effnetb2_total_params,\n",
    "                  \"model_size (MB)\":pretrained_effnetb2_model_size\n",
    "                  }\n",
    "effnetb2_stats\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uzew8mGWzWws"
   },
   "source": [
    "# Creating A ViT feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mjg94UDctEYU",
    "outputId": "ec96e86f-36ec-4417-b9be-380fb6ce12f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (head): Linear(in_features=768, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out ViT heads layer\n",
    "vit = torchvision.models.vit_b_16()\n",
    "vit.heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iyCbh_hftSPG"
   },
   "outputs": [],
   "source": [
    "def create_vit_model(num_classes: int=3,\n",
    "                     seed: int=42):\n",
    "  \"\"\" \n",
    "  Creates a ViT-B/16 feature extractor model and transforms\n",
    "  \"\"\"\n",
    "  # Creates ViT_B_16 pretrained weights, transfroms and model\n",
    "  weights = torchvision.models.ViT_B_16_Weights.DEFAULT\n",
    "  transforms = weights.transforms()\n",
    "  model = torchvision.models.vit_b_16(weights=weights)\n",
    "\n",
    "  # Freeze all layers in model\n",
    "  for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "  # Change classifier head to suit our needs\n",
    "  torch.manual_seed(42)\n",
    "  model.heads = nn.Sequential(nn.Linear(in_features=768,\n",
    "                                        out_features=num_classes))\n",
    "  \n",
    "  return model, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 65,
     "referenced_widgets": [
      "4c232c0d99cc4b9f916797a8e622a809",
      "6f900a18588547f89587f61e6c833575",
      "0cda129e20c4440fba52fea4d3a44b16",
      "7e04ee6b0e6a452b99d72a3dd6cae451",
      "13aec9269462480f9040dad1673efa9d",
      "0da1c1783ccd47e0a3f0ad471b602e12",
      "3309ebd093dd44bdab978580f7cc891c",
      "c01aa17ea24e48fc81f05e4cf6ca5ac4",
      "e94960023c4448d5af9e2eaffb9c3874",
      "cb447be6ae5b417e8e3fc33cab459dab",
      "62dd84b6a5e94ea5b101ba1af433784e"
     ]
    },
    "id": "RnmUQmaYvvXI",
    "outputId": "950d937b-6099-4404-a563-09d94970ce2c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vit_b_16-c867db91.pth\" to /root/.cache/torch/hub/checkpoints/vit_b_16-c867db91.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c232c0d99cc4b9f916797a8e622a809",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/330M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create ViT model and transforms\n",
    "vit, vit_transforms = create_vit_model(num_classes=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LZoTaXUNv-eu",
    "outputId": "212487f3-c0d6-457c-d364-bb01f4085116"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "============================================================================================================================================\n",
       "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
       "============================================================================================================================================\n",
       "VisionTransformer (VisionTransformer)                        [1, 3, 224, 224]     [1, 3]               768                  Partial\n",
       "├─Conv2d (conv_proj)                                         [1, 3, 224, 224]     [1, 768, 14, 14]     (590,592)            False\n",
       "├─Encoder (encoder)                                          [1, 197, 768]        [1, 197, 768]        151,296              False\n",
       "│    └─Dropout (dropout)                                     [1, 197, 768]        [1, 197, 768]        --                   --\n",
       "│    └─Sequential (layers)                                   [1, 197, 768]        [1, 197, 768]        --                   False\n",
       "│    │    └─EncoderBlock (encoder_layer_0)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_1)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_2)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_3)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_4)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_5)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_6)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_7)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_8)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_9)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_10)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_11)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    └─LayerNorm (ln)                                        [1, 197, 768]        [1, 197, 768]        (1,536)              False\n",
       "├─Sequential (heads)                                         [1, 768]             [1, 3]               --                   True\n",
       "│    └─Linear (0)                                            [1, 768]             [1, 3]               2,307                True\n",
       "============================================================================================================================================\n",
       "Total params: 85,800,963\n",
       "Trainable params: 2,307\n",
       "Non-trainable params: 85,798,656\n",
       "Total mult-adds (M): 172.47\n",
       "============================================================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 104.09\n",
       "Params size (MB): 229.20\n",
       "Estimated Total Size (MB): 333.89\n",
       "============================================================================================================================================"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "summary(vit,\n",
    "        input_size=(1, 3, 224, 224),\n",
    "        col_names=['input_size', 'output_size', 'num_params', 'trainable'],\n",
    "        col_width=20,\n",
    "        row_settings=['var_names'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "76TCsiOPwXMr"
   },
   "source": [
    "## Create  Dataloaders "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LSQWIs6qwdw7"
   },
   "outputs": [],
   "source": [
    "# Setup ViT Dataloaders\n",
    "\n",
    "from going_modular import data_setup \n",
    "\n",
    "train_dataloader_vit, test_dataloader_vit, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                       test_dir=test_dir,\n",
    "                                                                                       transform=vit_transforms,\n",
    "                                                                                       batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Szlbu9pw3pP"
   },
   "source": [
    "## Training ViT model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378,
     "referenced_widgets": [
      "b1b5315750ec4163bfbb09db59b0d51c",
      "57166e859d654b02b3bbeea921205afe",
      "47805df7930e4bb596ee76c826a9ca39",
      "efc7dd88fca749188a5919050cd559aa",
      "b74b60a8244345ab99439bba14a8f6fe",
      "dd0b79887a714cf3b8683325eb6e4941",
      "09d74e4e4e654ca5b1d8433ae0bb6e31",
      "3bc12f0a5764419a85b1f462b56458e9",
      "b0cb3e99d3f7495d8d70df54dc2bead1",
      "25568a2ec025423083be8489bcdbbbd9",
      "47df6ea5a175421180aa750656fbab9d"
     ]
    },
    "id": "kYLuNsvWw6u8",
    "outputId": "23811708-003d-41cc-ed33-cc1cb9d739d8"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1b5315750ec4163bfbb09db59b0d51c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.7023 | train_acc: 0.7500 | test_loss: 0.2714 | test_acc: 0.9290\n",
      "Epoch: 2 | train_loss: 0.2531 | train_acc: 0.9104 | test_loss: 0.1669 | test_acc: 0.9602\n",
      "Epoch: 3 | train_loss: 0.1766 | train_acc: 0.9542 | test_loss: 0.1270 | test_acc: 0.9693\n",
      "Epoch: 4 | train_loss: 0.1277 | train_acc: 0.9625 | test_loss: 0.1072 | test_acc: 0.9722\n",
      "Epoch: 5 | train_loss: 0.1163 | train_acc: 0.9646 | test_loss: 0.0950 | test_acc: 0.9784\n",
      "Epoch: 6 | train_loss: 0.1270 | train_acc: 0.9375 | test_loss: 0.0830 | test_acc: 0.9722\n",
      "Epoch: 7 | train_loss: 0.0899 | train_acc: 0.9771 | test_loss: 0.0844 | test_acc: 0.9784\n",
      "Epoch: 8 | train_loss: 0.0928 | train_acc: 0.9812 | test_loss: 0.0759 | test_acc: 0.9722\n",
      "Epoch: 9 | train_loss: 0.0933 | train_acc: 0.9792 | test_loss: 0.0729 | test_acc: 0.9784\n",
      "Epoch: 10 | train_loss: 0.0662 | train_acc: 0.9833 | test_loss: 0.0642 | test_acc: 0.9847\n",
      "Epoch: 11 | train_loss: 0.0593 | train_acc: 0.9854 | test_loss: 0.0600 | test_acc: 0.9938\n",
      "Epoch: 12 | train_loss: 0.0535 | train_acc: 0.9938 | test_loss: 0.0609 | test_acc: 0.9784\n",
      "Epoch: 13 | train_loss: 0.0463 | train_acc: 0.9958 | test_loss: 0.0615 | test_acc: 0.9784\n",
      "Epoch: 14 | train_loss: 0.0462 | train_acc: 0.9958 | test_loss: 0.0597 | test_acc: 0.9784\n",
      "Epoch: 15 | train_loss: 0.0398 | train_acc: 0.9958 | test_loss: 0.0593 | test_acc: 0.9722\n",
      "Epoch: 16 | train_loss: 0.0369 | train_acc: 0.9958 | test_loss: 0.0576 | test_acc: 0.9784\n",
      "Epoch: 17 | train_loss: 0.0330 | train_acc: 0.9958 | test_loss: 0.0575 | test_acc: 0.9784\n",
      "Epoch: 18 | train_loss: 0.0314 | train_acc: 0.9979 | test_loss: 0.0568 | test_acc: 0.9784\n",
      "Epoch: 19 | train_loss: 0.0299 | train_acc: 0.9979 | test_loss: 0.0564 | test_acc: 0.9722\n",
      "Epoch: 20 | train_loss: 0.0313 | train_acc: 1.0000 | test_loss: 0.0560 | test_acc: 0.9722\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine \n",
    "\n",
    "optimizer = torch.optim.Adam(params=vit.parameters(),\n",
    "                            lr=0.001)\n",
    "\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "set_seeds()\n",
    "\n",
    "vit_results = engine.train(model=vit,\n",
    "                           train_dataloader=train_dataloader_vit,\n",
    "                           test_dataloader=test_dataloader_vit,\n",
    "                           epochs=20,\n",
    "                           optimizer=optimizer,\n",
    "                           loss_fn=loss_fn,\n",
    "                           device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QuORjhkexzRL"
   },
   "source": [
    "## Inspecting ViT loss curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "w-SwrkFGx8D8",
    "outputId": "30bbbc83-bc51-4545-8558-5fa73a84a1bd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(vit_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cmirWp5typYm"
   },
   "source": [
    "## Saving ViT feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eS_AGnbky2-z",
    "outputId": "0bed0965-f232-4a82-b6ac-a24b88c6227d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\n"
     ]
    }
   ],
   "source": [
    "# Save the model\n",
    "from going_modular import utils\n",
    "\n",
    "utils.save_model(model=vit,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=\"09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_wo_qe5ty62c"
   },
   "source": [
    "## Checking the size of ViT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iB2wVdAGzA2n",
    "outputId": "18e2f43e-f0b9-4f4f-ed95-f2b8d5d32fe6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrained ViT feature extractor model size: 327 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "pretrained_vit_model_size = Path(\"models/09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\").stat().st_size // (1024*1024) # division converts bytes to megabytes (roughly) \n",
    "print(f\"Pretrained ViT feature extractor model size: {pretrained_vit_model_size} MB\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WhTfAX65zGsC"
   },
   "source": [
    "## Collecting ViT stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "caaIve1UzW7Z",
    "outputId": "995078dd-49da-43ce-b95c-172ec26287ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85800963"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of parameters in ViT\n",
    "\n",
    "vit_total_params = sum(torch.numel(param) for param in vit.parameters())\n",
    "vit_total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AkbBCjivzroE",
    "outputId": "e95080cb-1b26-472a-ecd4-af8852cd50e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.05601070134434849,\n",
       " 'test_acc': 0.972159090909091,\n",
       " 'number_of_parameters': 85800963,\n",
       " 'model_size (MB)': 327}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vit_stats = {'test_loss': vit_results['test_loss'][-1],\n",
    "             'test_acc': vit_results['test_acc'][-1],\n",
    "             'number_of_parameters': vit_total_params,\n",
    "             'model_size (MB)': pretrained_vit_model_size\n",
    "             }\n",
    "vit_stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BdJBR0-80NhL"
   },
   "source": [
    "# Making predictions with trained models and timing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GIyDWJTw0llS",
    "outputId": "de3aaf0e-914d-4c1a-e4f3-06811ecb4c39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Finding all filepaths ending with '.jpg' in directory: data/pizza_steak_sushi_20_percent/test\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('data/pizza_steak_sushi_20_percent/test/steak/1482489.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/steak/3541033.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/steak/3100563.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/steak/670345.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/steak/3873655.jpg')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path \n",
    "\n",
    "# Get all test data paths\n",
    "\n",
    "print(f\"[INFO] Finding all filepaths ending with '.jpg' in directory: {test_dir}\")\n",
    "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
    "test_data_paths[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SmpvMe2e1Ecd"
   },
   "source": [
    "## Creating a function to make predictions across the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tF6wh-MN1POI"
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import torch\n",
    "\n",
    "from PIL import Image \n",
    "from timeit import default_timer as timer \n",
    "from tqdm.auto import tqdm \n",
    "from typing import List, Dict\n",
    "\n",
    "# Create a  function to return a list of dictionaries with\n",
    "# sample, true_label, pred_label, predict_proba and prediction time\n",
    "\n",
    "def pred_and_store(paths: List[pathlib.Path],\n",
    "                   model: torch.nn.Module,\n",
    "                   transform: torchvision.transforms,\n",
    "                   class_names: List[str],\n",
    "                   device: str = 'cuda' if torch.cuda.is_available() else 'cpu') -> List[Dict]:\n",
    "  \n",
    "  # Create an empty list\n",
    "  pred_list = []\n",
    "\n",
    "  # Loop through target paths\n",
    "  for path in tqdm(paths):\n",
    "    \n",
    "    # Create empty dictionary to store prediction information for each sample\n",
    "    pred_dict = {}\n",
    "\n",
    "    # Get the sample path and ground truth class name\n",
    "    pred_dict['image_path'] = path\n",
    "    class_name = path.parent.stem \n",
    "    pred_dict['class_name'] = class_name\n",
    "\n",
    "    # Start the prediction timer\n",
    "    start_time = timer()\n",
    "\n",
    "    # Open image path \n",
    "    img = Image.open(path)\n",
    "\n",
    "    # Transform the image, add batch dimension an put image on target device\n",
    "    transformed_image = transform(img).unsqueeze(0).to(device)\n",
    "\n",
    "    # Prepare model for inference by sending it to target device and turning on eval() model\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "\n",
    "    # Get prediction probabilty, pred_label and pred_class\n",
    "    with torch.inference_mode():\n",
    "      pred_logit = model(transformed_image)\n",
    "      pred_prob = torch.softmax(pred_logit, dim=1)\n",
    "      pred_label = torch.argmax(pred_prob, dim=1)\n",
    "      pred_class = class_names[pred_label.cpu()]\n",
    "\n",
    "      # Make sure things in the directory are on CPU\n",
    "      pred_dict['pred_prob'] = round(pred_prob.unsqueeze(0).max().cpu().item(), 4)\n",
    "      pred_dict['pred_class'] = pred_class \n",
    "\n",
    "      # end timer and calculate time per pred\n",
    "      end_time = timer()\n",
    "      pred_dict['time_for_pred'] = round(end_time-start_time, 4)\n",
    "\n",
    "    # Does the pred match the true label\n",
    "    pred_dict['correct'] = class_name == pred_class\n",
    "\n",
    "    # Add the dictionary to list\n",
    "    pred_list.append(pred_dict)\n",
    "\n",
    "  return pred_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_LfAQfzG5fcO"
   },
   "source": [
    "## Making and timing predictions with EffNetB2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "8b5b5ff96b784a5587dc3ef0f3642170",
      "fdf42b71dfa742bcbdf28367cb490407",
      "0b8b0d9f1af74901bcd9cc4fb1c7e6ca",
      "1e7a027302fd4880b475ddf025bf9560",
      "81bccdc7977e4b658418242ce507c49a",
      "c230d4cbe47c4fac8a9181897df86a37",
      "d2aff66eafd64e3daa90106f43d5f7d2",
      "801a1720b890414f9bc9e79632607e21",
      "34e97ca277f7466a86468934d23ae437",
      "20802249c3e5499e8711ee95ad993880",
      "62d6bdda54b74449bdf62b17fcad9313"
     ]
    },
    "id": "12XK7Q2c5t9T",
    "outputId": "1c4b87f8-a669-4d93-b433-8723aea6984c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b5b5ff96b784a5587dc3ef0f3642170",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions across test dataset\n",
    "effnetb2_test_pred_dicts  = pred_and_store(paths=test_data_paths,\n",
    "                                           model=effnetb2,\n",
    "                                           transform=effnetb2_transforms,\n",
    "                                           class_names=class_names,\n",
    "                                           device='cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mk96VYqb6RfH",
    "outputId": "f3b3dccb-68c2-40f8-e70e-a07c67d0f591"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/steak/1482489.jpg'),\n",
       "  'class_name': 'steak',\n",
       "  'pred_prob': 1.0,\n",
       "  'pred_class': 'steak',\n",
       "  'time_for_pred': 0.3239,\n",
       "  'correct': True},\n",
       " {'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/steak/3541033.jpg'),\n",
       "  'class_name': 'steak',\n",
       "  'pred_prob': 1.0,\n",
       "  'pred_class': 'steak',\n",
       "  'time_for_pred': 0.1029,\n",
       "  'correct': True}]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the first 2 prediction \n",
    "effnetb2_test_pred_dicts[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "QVwBFSyK6e4t",
    "outputId": "c93a4ab9-cc49-4a89-ee77-bc1c58f7b04b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-c11ad139-0e97-4a69-8da8-48e5c19aa38c\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>class_name</th>\n",
       "      <th>pred_prob</th>\n",
       "      <th>pred_class</th>\n",
       "      <th>time_for_pred</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/1...</td>\n",
       "      <td>steak</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.3239</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/3...</td>\n",
       "      <td>steak</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.1029</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/3...</td>\n",
       "      <td>steak</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.1051</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/6...</td>\n",
       "      <td>steak</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.1074</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/3...</td>\n",
       "      <td>steak</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.1115</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/2...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9850</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0928</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/8...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9963</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.1095</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/7...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.8611</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.0994</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c11ad139-0e97-4a69-8da8-48e5c19aa38c')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-c11ad139-0e97-4a69-8da8-48e5c19aa38c button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-c11ad139-0e97-4a69-8da8-48e5c19aa38c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                                            image_path class_name  pred_prob  \\\n",
       "0    data/pizza_steak_sushi_20_percent/test/steak/1...      steak     1.0000   \n",
       "1    data/pizza_steak_sushi_20_percent/test/steak/3...      steak     1.0000   \n",
       "2    data/pizza_steak_sushi_20_percent/test/steak/3...      steak     1.0000   \n",
       "3    data/pizza_steak_sushi_20_percent/test/steak/6...      steak     1.0000   \n",
       "4    data/pizza_steak_sushi_20_percent/test/steak/3...      steak     1.0000   \n",
       "..                                                 ...        ...        ...   \n",
       "145  data/pizza_steak_sushi_20_percent/test/sushi/2...      sushi     0.9850   \n",
       "146  data/pizza_steak_sushi_20_percent/test/sushi/8...      sushi     0.9993   \n",
       "147  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.9963   \n",
       "148  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.9986   \n",
       "149  data/pizza_steak_sushi_20_percent/test/sushi/7...      sushi     0.8611   \n",
       "\n",
       "    pred_class  time_for_pred  correct  \n",
       "0        steak         0.3239     True  \n",
       "1        steak         0.1029     True  \n",
       "2        steak         0.1051     True  \n",
       "3        steak         0.1074     True  \n",
       "4        steak         0.1115     True  \n",
       "..         ...            ...      ...  \n",
       "145      sushi         0.0928     True  \n",
       "146      sushi         0.0959     True  \n",
       "147      sushi         0.1095     True  \n",
       "148      sushi         0.1045     True  \n",
       "149      steak         0.0994    False  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the test_pred_dicts into a DataFrame\n",
    "import pandas as pd\n",
    "\n",
    "effnetb2_test_pred_df = pd.DataFrame(effnetb2_test_pred_dicts)\n",
    "effnetb2_test_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c3kXO4HO64FW",
    "outputId": "9e96173a-708c-487e-cb62-ac85ff32de01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     143\n",
       "False      7\n",
       "Name: correct, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of correct preds\n",
    "effnetb2_test_pred_df.correct.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6l2lzZB87GC1",
    "outputId": "4ef697f5-d9af-4574-9c7d-c2095e28f154"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EffNetB2 average time per prediction: 0.1114 seconds\n"
     ]
    }
   ],
   "source": [
    "# Find average time per prediction\n",
    "effnetb2_average_time_per_pred = round(effnetb2_test_pred_df.time_for_pred.mean(), 4)\n",
    "print(f\"EffNetB2 average time per prediction: {effnetb2_average_time_per_pred} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DtGFtgmaB1ME",
    "outputId": "d4fbeca2-9786-4e1a-cc48-beab6b470212"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.1993967060931027,\n",
       " 'test_acc': 0.9477272727272726,\n",
       " 'number_of_parameters': 7705221,\n",
       " 'model_size (MB)': 29,\n",
       " 'time_per_pred_cpu': 0.1114}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add EffNertB2 average prediction time to stats dictionary\n",
    "effnetb2_stats['time_per_pred_cpu'] = effnetb2_average_time_per_pred\n",
    "effnetb2_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "opvKLZ-4CbAa"
   },
   "source": [
    "## Making an timing predicts with ViT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "8374c645d1e84187be3b836eb8bfbb7a",
      "dfa9c86245744629889ed3a79c5cc488",
      "17b794d99d5d4c618428f58814bb0ed7",
      "9e488c65f67f41a29b4ab1ac2acf4940",
      "33c6e17ddb83403bb4241f66d4196f33",
      "17845d86c96c44f6b883fddbe5c1f133",
      "f9fffe83602b42148e53e11abc79cdbc",
      "6837fca5db5240d899914b02a598269b",
      "eedb9d4867404497b2dd13b5f2487158",
      "04006c1e764442c7927270dca7108800",
      "f891f9c1064f4d9da7ad730eb307fa52"
     ]
    },
    "id": "Jvp-Q-3nEMph",
    "outputId": "df6555f3-8b27-4c8e-f31c-7c1147cd91e7"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8374c645d1e84187be3b836eb8bfbb7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vit_test_pred_dicts = pred_and_store(paths=test_data_paths,\n",
    "                                     model=vit,\n",
    "                                     transform=vit_transforms,\n",
    "                                     class_names=class_names,\n",
    "                                     device='cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BmZw33rrEho7",
    "outputId": "435284bf-04f4-4863-e5cb-b071cc55128b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/steak/1482489.jpg'),\n",
       "  'class_name': 'steak',\n",
       "  'pred_prob': 0.9993,\n",
       "  'pred_class': 'steak',\n",
       "  'time_for_pred': 0.7134,\n",
       "  'correct': True},\n",
       " {'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/steak/3541033.jpg'),\n",
       "  'class_name': 'steak',\n",
       "  'pred_prob': 0.5472,\n",
       "  'pred_class': 'steak',\n",
       "  'time_for_pred': 0.8935,\n",
       "  'correct': True}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 2 predictions\n",
    "vit_test_pred_dicts[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "7KOUP1MNEvT2",
    "outputId": "fba5fa8c-e9fe-4c06-d6e4-7009cd6adeea"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-0a0fc4f1-7e84-47dc-b4fc-314f200c8798\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>class_name</th>\n",
       "      <th>pred_prob</th>\n",
       "      <th>pred_class</th>\n",
       "      <th>time_for_pred</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/1...</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.7134</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/3...</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.5472</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.8935</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/3...</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.8901</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/6...</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.9998</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.8843</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/steak/3...</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>steak</td>\n",
       "      <td>0.8615</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/2...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9304</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.5169</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/8...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9865</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.5193</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9941</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.5150</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9912</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.5562</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/7...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9575</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.5149</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a0fc4f1-7e84-47dc-b4fc-314f200c8798')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-0a0fc4f1-7e84-47dc-b4fc-314f200c8798 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-0a0fc4f1-7e84-47dc-b4fc-314f200c8798');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                                            image_path class_name  pred_prob  \\\n",
       "0    data/pizza_steak_sushi_20_percent/test/steak/1...      steak     0.9993   \n",
       "1    data/pizza_steak_sushi_20_percent/test/steak/3...      steak     0.5472   \n",
       "2    data/pizza_steak_sushi_20_percent/test/steak/3...      steak     0.9968   \n",
       "3    data/pizza_steak_sushi_20_percent/test/steak/6...      steak     0.9998   \n",
       "4    data/pizza_steak_sushi_20_percent/test/steak/3...      steak     0.9986   \n",
       "..                                                 ...        ...        ...   \n",
       "145  data/pizza_steak_sushi_20_percent/test/sushi/2...      sushi     0.9304   \n",
       "146  data/pizza_steak_sushi_20_percent/test/sushi/8...      sushi     0.9865   \n",
       "147  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.9941   \n",
       "148  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.9912   \n",
       "149  data/pizza_steak_sushi_20_percent/test/sushi/7...      sushi     0.9575   \n",
       "\n",
       "    pred_class  time_for_pred  correct  \n",
       "0        steak         0.7134     True  \n",
       "1        steak         0.8935     True  \n",
       "2        steak         0.8901     True  \n",
       "3        steak         0.8843     True  \n",
       "4        steak         0.8615     True  \n",
       "..         ...            ...      ...  \n",
       "145      sushi         0.5169     True  \n",
       "146      sushi         0.5193     True  \n",
       "147      sushi         0.5150     True  \n",
       "148      sushi         0.5562     True  \n",
       "149      sushi         0.5149     True  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn vit_test_pred_dicts into DataFrame\n",
    "\n",
    "vit_test_pred_df = pd.DataFrame(vit_test_pred_dicts)\n",
    "vit_test_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-pfqcfYnFBoy",
    "outputId": "cd2f438a-3251-4755-f1cd-b2f80b41c982"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     146\n",
       "False      4\n",
       "Name: correct, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check corrects\n",
    "vit_test_pred_df.correct.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FeUgXfQnFKa_",
    "outputId": "d5df6207-db59-4f7c-dc80-b5e1ea2e4adf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ViT average time per prediction: 0.6118 seconds\n"
     ]
    }
   ],
   "source": [
    "# Calculate average time per prediction for ViT model\n",
    "vit_average_time_per_pred = round(vit_test_pred_df.time_for_pred.mean(), 4)\n",
    "print(f\"ViT average time per prediction: {vit_average_time_per_pred} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vkKvwUKGGIR5",
    "outputId": "5a04cd38-be0b-46e5-f9fe-b87fd8d34f5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.05601070134434849,\n",
       " 'test_acc': 0.972159090909091,\n",
       " 'number_of_parameters': 85800963,\n",
       " 'model_size (MB)': 327,\n",
       " 'time_per_pred_cpu': 0.6118}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vit_stats['time_per_pred_cpu'] = vit_average_time_per_pred\n",
    "vit_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qCH_l2tCFOD-"
   },
   "source": [
    "# Comparing model results, predicton times ans size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 109
    },
    "id": "mUnicCjGFVPO",
    "outputId": "1769c8c3-f923-41e0-b79f-ddf4ca6e40a2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-030b422f-983b-4f63-aa15-30691d9d1e8d\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>number_of_parameters</th>\n",
       "      <th>model_size (MB)</th>\n",
       "      <th>time_per_pred_cpu</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.199397</td>\n",
       "      <td>94.77</td>\n",
       "      <td>7705221</td>\n",
       "      <td>29</td>\n",
       "      <td>0.1114</td>\n",
       "      <td>EffNetB2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.056011</td>\n",
       "      <td>97.22</td>\n",
       "      <td>85800963</td>\n",
       "      <td>327</td>\n",
       "      <td>0.6118</td>\n",
       "      <td>ViT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-030b422f-983b-4f63-aa15-30691d9d1e8d')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-030b422f-983b-4f63-aa15-30691d9d1e8d button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-030b422f-983b-4f63-aa15-30691d9d1e8d');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   test_loss  test_acc  number_of_parameters  model_size (MB)  \\\n",
       "0   0.199397     94.77               7705221               29   \n",
       "1   0.056011     97.22              85800963              327   \n",
       "\n",
       "   time_per_pred_cpu     model  \n",
       "0             0.1114  EffNetB2  \n",
       "1             0.6118       ViT  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn stat dicts into DataFrame\n",
    "df = pd.DataFrame([effnetb2_stats,vit_stats])\n",
    "df['model'] = ['EffNetB2', 'ViT']\n",
    "\n",
    "# Convert accuracy to percentage\n",
    "df['test_acc'] = round(df['test_acc'] * 100, 2)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 78
    },
    "id": "Hb8O_kSHFzcQ",
    "outputId": "38af4136-1ffb-4a15-f26d-56d4ac2b85ea"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-ba9df628-f450-49da-84b0-7421c478d8ce\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>number_of_parameters</th>\n",
       "      <th>model_size (MB)</th>\n",
       "      <th>time_per_pred_cpu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ViT to EffNetB2 ratios</th>\n",
       "      <td>0.280901</td>\n",
       "      <td>1.025852</td>\n",
       "      <td>11.135432</td>\n",
       "      <td>11.275862</td>\n",
       "      <td>5.491921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba9df628-f450-49da-84b0-7421c478d8ce')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-ba9df628-f450-49da-84b0-7421c478d8ce button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ba9df628-f450-49da-84b0-7421c478d8ce');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                        test_loss  test_acc  number_of_parameters  \\\n",
       "ViT to EffNetB2 ratios   0.280901  1.025852             11.135432   \n",
       "\n",
       "                        model_size (MB)  time_per_pred_cpu  \n",
       "ViT to EffNetB2 ratios        11.275862           5.491921  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare ViT to EffNetB2 across different characterisrics\n",
    "pd.DataFrame(data=(df.set_index('model').loc['ViT'] / df.set_index('model').loc['EffNetB2']),\n",
    "             columns=['ViT to EffNetB2 ratios']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EoqIEqoBEJTO"
   },
   "source": [
    "## Visualizing the speed vs. performance tradeoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 523
    },
    "id": "SuIL1bpzEYW5",
    "outputId": "8b390107-9093-4ee8-bc9b-f735c1dac3f4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Create a plot from model comparison DataFrame\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "scatter = ax.scatter(data=df, \n",
    "                     x=\"time_per_pred_cpu\", \n",
    "                     y=\"test_acc\", \n",
    "                     c=[\"blue\", \"orange\"], # what colours to use?\n",
    "                     s=\"model_size (MB)\") # size the dots by the model sizes\n",
    "\n",
    "# 2. Add titles, labels and customize fontsize for aesthetics\n",
    "ax.set_title(\"FoodVision Mini Inference Speed vs Performance\", fontsize=18)\n",
    "ax.set_xlabel(\"Prediction time per image (seconds)\", fontsize=14)\n",
    "ax.set_ylabel(\"Test accuracy (%)\", fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "ax.grid(True)\n",
    "\n",
    "# 3. Annotate with model names\n",
    "for index, row in df.iterrows():\n",
    "    ax.annotate(text=row[\"model\"], # note: depending on your version of Matplotlib, you may need to use \"s=...\" or \"text=...\", see: https://github.com/faustomorales/keras-ocr/issues/183#issuecomment-977733270 \n",
    "                xy=(row[\"time_per_pred_cpu\"]+0.0006, row[\"test_acc\"]+0.03),\n",
    "                size=12)\n",
    "\n",
    "# 4. Create a legend based on model sizes\n",
    "handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.5)\n",
    "model_size_legend = ax.legend(handles, \n",
    "                              labels, \n",
    "                              loc=\"lower right\", \n",
    "                              title=\"Model size (MB)\",\n",
    "                              fontsize=12)\n",
    "\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D96PzOcHFYl3"
   },
   "source": [
    "# Bringing FoodVision Mini to life by creating a Gradio demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-vsgmJWWGjgq",
    "outputId": "d7db98f0-616c-4d40-9436-bca34461e649"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.3/14.3 MB\u001b[0m \u001b[31m83.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m55.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 KB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 KB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 KB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 KB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 KB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.8/57.8 KB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 KB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m87.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.9/140.9 KB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 KB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m264.6/264.6 KB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 KB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 KB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/69.6 KB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 KB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.6/80.6 KB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Gradio version: 3.20.1\n"
     ]
    }
   ],
   "source": [
    "# Import/install Gradio\n",
    "try:\n",
    "  import gradio as gr\n",
    "except:\n",
    "  !pip -q install gradio\n",
    "  import gradio as gr\n",
    "\n",
    "print(f'Gradio version: {gr.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J6Jw7B17G2rr"
   },
   "source": [
    "## Creating a function to map inputs and outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MD6rC0vyG7Ky",
    "outputId": "6470152a-23c8-47ee-bcec-d828af26e5dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put EffNetB2 on CPU\n",
    "effnet_b2.to('cpu')\n",
    "\n",
    "# Check the device\n",
    "next(iter(effnet_b2.parameters())).device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hJaajEaOH69M"
   },
   "source": [
    "## Create `predict()` function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SE0CuXyKIZiT"
   },
   "outputs": [],
   "source": [
    "from typing import Tuple, Dict\n",
    "\n",
    "def predict(img) ->Tuple[Dict, float]:\n",
    "  \"\"\"\n",
    "  Transforms nad performs a prediction on img and returns prediction and time taken\n",
    "  \"\"\"\n",
    "  # Start the timer\n",
    "  start_time = timer()\n",
    "\n",
    "  # Transform the target image and add a batch dimension\n",
    "  img = effnetb2_transforms(img).unsqueeze(0)\n",
    "\n",
    "  # Put model into evaluation mode and turn on inference mode\n",
    "  effnetb2.eval()\n",
    "  with torch.inference_mode():\n",
    "    # Pass the transformed image through the model and \n",
    "    # turn the prediction logits into predict proba\n",
    "    pred_probs = torch.softmax(effnetb2(img), dim=1)\n",
    "\n",
    "  # Create a prediction label and predict proba dictionary\n",
    "  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
    "\n",
    "  # Calculate the prediction time\n",
    "  pred_time = round(timer() - start_time, 5)\n",
    "\n",
    "  # Return the prediction dict and pred time\n",
    "  return pred_labels_and_probs, pred_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nZHk-ea7M6TY",
    "outputId": "f41286e6-e2e1-4514-eb85-c753143314cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Predicting on image at path: data/pizza_steak_sushi_20_percent/test/pizza/724290.jpg\n",
      "\n",
      "Prediction label and probability dictionary: \n",
      "{'pizza': 0.9938246011734009, 'steak': 0.0009674896136857569, 'sushi': 0.005207938142120838}\n",
      "Prediction time: 0.10565 seconds\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "from PIL import Image\n",
    "\n",
    "# Get a list of all test image filepaths\n",
    "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
    "\n",
    "# Randomly select a test image path\n",
    "random_image_path = random.sample(test_data_paths, k=1)[0]\n",
    "\n",
    "# Open the target image\n",
    "image = Image.open(random_image_path)\n",
    "print(f\"[INFO] Predicting on image at path: {random_image_path}\\n\")\n",
    "\n",
    "# Predict on the target image and print out the outputs\n",
    "pred_dict, pred_time = predict(img=image)\n",
    "print(f\"Prediction label and probability dictionary: \\n{pred_dict}\")\n",
    "print(f\"Prediction time: {pred_time} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a8Qb4zQ8PHrj"
   },
   "source": [
    "## Creating a list of examples images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "maxUPSmvPOL9",
    "outputId": "2794861a-fe2b-4536-f0b6-42bd7d725ba1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['data/pizza_steak_sushi_20_percent/test/sushi/3177743.jpg'],\n",
       " ['data/pizza_steak_sushi_20_percent/test/steak/1335842.jpg'],\n",
       " ['data/pizza_steak_sushi_20_percent/test/steak/86782.jpg']]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of examples inputs to Gradio demo\n",
    "example_list = [[str(filepath)] for filepath in random.sample(test_data_paths, k=3)]\n",
    "example_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LnRANkb1kLZd"
   },
   "source": [
    "## Building a Gradio interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "id": "_JrXqB1gkP-t",
    "outputId": "82a3784e-dd63-47a9-e2d5-171491246863"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
      "Running on public URL: https://b269bccbb68174e41b.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://b269bccbb68174e41b.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "# Create title, description and article strings\n",
    "title = 'FoodVision Mini 🍕🥩🍣'\n",
    "description = \"An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi.\"\n",
    "article = \"Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/).\"\n",
    "\n",
    "# Create the Gradio demo\n",
    "demo = gr.Interface(fn=predict, # mapping function from input to output\n",
    "                    inputs=gr.Image(type='pil'), # type of inputs\n",
    "                    outputs=[gr.Label(num_top_classes=3, label='Predictions'), # type of outputs\n",
    "                             gr.Number(label='Prediction time (s)')],\n",
    "                    examples=example_list,\n",
    "                    title=title,\n",
    "                    description=description,\n",
    "                    article=article)\n",
    "# Launch the demo\n",
    "\n",
    "demo.launch(debug=False, \n",
    "            share=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pu-xwoW74aXq"
   },
   "source": [
    "# Turning FoodVision Mini Gradio Demo into a deployable app"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AdM_lj5i4-Lr"
   },
   "source": [
    "## Creating a `demos` folder to store FoodVision Mini app files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "r5CKkG575w2p"
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "from pathlib import Path\n",
    "\n",
    "# Create FoodVision mini demo path\n",
    "foodvision_mini_demo_path = Path('demos/foodvision_mini/')\n",
    "\n",
    "# Remove files that might already exists there and create new directory\n",
    "if foodvision_mini_demo_path.exists():\n",
    "  shutil.rmtree(foodvision_mini_demo_path)\n",
    "  foodvision_mini_demo_path.mkdir(parents=True,\n",
    "                                  exist_ok=True)\n",
    "else:\n",
    "  # If the file doesn't exists, create it anyway\n",
    "  foodvision_mini_demo_path.mkdir(parents=True,\n",
    "                                  exist_ok=True)\n",
    "  \n",
    "!ls demos/foodvision_mini/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Mf0-POvmaXM"
   },
   "source": [
    "## Creating a folder of examples images to use with FoodVision Mini demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7k9IDFwk8tGq",
    "outputId": "f6695cb3-172b-4ffc-9f7d-47f9429bb5d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Copying data/pizza_steak_sushi_20_percent/test/sushi/592799.jpg to demos/foodvision_mini/examples/592799.jpg\n",
      "[INFO] Copying data/pizza_steak_sushi_20_percent/test/steak/3622237.jpg to demos/foodvision_mini/examples/3622237.jpg\n",
      "[INFO] Copying data/pizza_steak_sushi_20_percent/test/pizza/2582289.jpg to demos/foodvision_mini/examples/2582289.jpg\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "from pathlib import Path\n",
    "\n",
    "# 1. Create an examples directory\n",
    "foodvision_mini_examples_path = foodvision_mini_demo_path / \"examples\"\n",
    "foodvision_mini_examples_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# 2. Collect three random test dataset image paths\n",
    "foodvision_mini_examples = [Path('data/pizza_steak_sushi_20_percent/test/sushi/592799.jpg'),\n",
    "                            Path('data/pizza_steak_sushi_20_percent/test/steak/3622237.jpg'),\n",
    "                            Path('data/pizza_steak_sushi_20_percent/test/pizza/2582289.jpg')]\n",
    "\n",
    "# 3. Copy the three random images to the examples directory\n",
    "for example in foodvision_mini_examples:\n",
    "    destination = foodvision_mini_examples_path / example.name\n",
    "    print(f\"[INFO] Copying {example} to {destination}\")\n",
    "    shutil.copy2(src=example, dst=destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KbvMXguU9jrt",
    "outputId": "67524a69-1db3-4b8b-a255-1f25e4c90e30"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['example/2582289.jpg'], ['example/3622237.jpg'], ['example/592799.jpg']]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "# get examples filepaths in a list of lists\n",
    "example_list = [['example/'+ example] for example in os.listdir(foodvision_mini_examples_path)]\n",
    "example_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J3q_v1XF97k2",
    "outputId": "19a2e59d-41df-4daa-d567-7d4cc88ebd35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Attempting to move models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth to demos/foodvision_mini/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n",
      "[INFO] Model move complete.\n"
     ]
    }
   ],
   "source": [
    "## Moving trained EffnetB2 model to FoodVission Mini demo directory\n",
    "import shutil\n",
    "\n",
    "# Create a source path for target model\n",
    "effnetb2_foodvision_mini_model_path = 'models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth'\n",
    "\n",
    "# Create a destination path for target model\n",
    "\n",
    "effnetb2_foodvision_mini_model_destination = foodvision_mini_demo_path / effnetb2_foodvision_mini_model_path.split('/')[1]\n",
    "\n",
    "# Try to move the file\n",
    "\n",
    "try:\n",
    "  print(f\"[INFO] Attempting to move {effnetb2_foodvision_mini_model_path} to {effnetb2_foodvision_mini_model_destination}\")\n",
    "\n",
    "  # Move the model\n",
    "  shutil.move(src=effnetb2_foodvision_mini_model_path,\n",
    "              dst=effnetb2_foodvision_mini_model_destination)\n",
    "  print(f\"[INFO] Model move complete.\")\n",
    "\n",
    "# If the model has already been moved, check if it exists\n",
    "except:\n",
    "  print(f\"[INFO] No model found at {effnetb2_foodvision_mini_model_path}, paerhaps its already been moved\")\n",
    "  print(f\"[INFO] Model exists at {effnetb2_foodvision_mini_model_destination}: {effnetb2_foodvision_mini_model_destination.exists()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JOei5pzVABVm"
   },
   "source": [
    "## Turning EffNetB2 model into a Python script(model.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fryAm1XwANvd",
    "outputId": "b0966f58-6a84-4860-962d-89fb0aa085ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_mini/model.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_mini/model.py\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "\n",
    "def create_effnetb2_model(num_classes: int=3,seed: int=42):\n",
    "  \"\"\"\n",
    "  Creates an  EfficientNetB2 feature extractor model and transforms\n",
    "  \"\"\"\n",
    "  # Create EffNetB2 pretrained weights, transfroms and model\n",
    "  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "  transforms = weights.transform()\n",
    "  model = torchvision.models.efficientnet_b2(weights=weights)\n",
    "\n",
    "  # Freeze all layers in base model\n",
    "  for param in model.parameters():\n",
    "    param.requires_grad=False\n",
    "\n",
    "  # Change classifier head with random seed for reproducibility \n",
    "  torch.manual_seed(seed)\n",
    "  model.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.3, inplace=True),\n",
    "    nn.Linear(in_features=1408, out_features=num_classes)\n",
    "  )\n",
    "\n",
    "  return model, transforms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XGesS4huCISr"
   },
   "source": [
    "## Turning FoodVision Mini Gradio app into a Python script (app.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WA3_2Vg0CU8R",
    "outputId": "5d4c5a1a-a7f2-48f3-d406-d7377c16c62f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_mini/app.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_mini/app.py\n",
    "\n",
    "import gradio as gr\n",
    "import os\n",
    "import torch \n",
    "\n",
    "from model import create_effnetb2_model\n",
    "from timeit import default_timer as timer\n",
    "from typing import Tuple, Dict\n",
    "\n",
    "# Setup class names\n",
    "class_names = ['pizza', 'steak', 'sushi']\n",
    "\n",
    "### Model and transforms preparation ###\n",
    "\n",
    "effnet_b2, effnetb2_transforms = create_effnetb2_model(num_classes=3)\n",
    "\n",
    "# Load a saved weights\n",
    "effnet_b2.load_state_dict(\n",
    "  torch.load(\n",
    "    f='09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth'',\n",
    "    map_location=torch.device('cpu')\n",
    "  )\n",
    ")\n",
    "\n",
    "### Predict function ###\n",
    "\n",
    "# Create predict function\n",
    "def predict(img) -> Tuple[Dict, float]:\n",
    "  \"\"\"\n",
    "  Transforms and performs a prediction on img and returns\n",
    "  prediction and time taken\n",
    "  \"\"\"\n",
    "\n",
    "  # Start timer\n",
    "  start_timer = timer()\n",
    "\n",
    "  # Transform the target image and add a batch dimension\n",
    "  img = effnetb2_transforms(img).unsqueeze(0)\n",
    "\n",
    "  # Put model into evaluation mode and turn on inference mode\n",
    "  effnet_b2.eval()\n",
    "  with torch.inference_mode():\n",
    "    # Pass the transformed image through the model and turn the \n",
    "    # prediction logits into predict_proba\n",
    "    pred_probs = torch.softmax(effnet_b2(img), dim=1)\n",
    "\n",
    "  # Create  a prediction label and predict_proba dictionary for each predict class\n",
    "  pred_labels_and_probs = {class_names[i]:float(pred_probs[0][i]) for i in range(len(class_names))}\n",
    "\n",
    "  # Calculate the prediction time\n",
    "  pred_time = round(timer() - start_timer, 5)\n",
    "\n",
    "  # Return the prediction dictionary and prediction time\n",
    "  return pred_labels_and_probs, pred_time\n",
    "\n",
    "### Gradio app ###\n",
    "\n",
    "title = 'FoodVision Big 👁️🍛'\n",
    "description = \"An EfficientNetB2 feature extractor computer vision model to classify images of food as pizz, steak, or sushi.\"\n",
    "article = \"Created at https://github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision\"\n",
    "\n",
    "# Create examples list from 'examples/' directory\n",
    "\n",
    "example_list = [['examples/'+ example] for example in os.listdir('examples')]\n",
    "\n",
    "# Create the Gradio\n",
    "demo = gr.Interface(fn=predict, \n",
    "                    inputs=gr.Image(type='pil'),\n",
    "                    outputs=[gr.Label(num_top_classes=3, label='Predictions'),\n",
    "                    gr.Number(label=\"Prediction time (s)\")],\n",
    "                    # Creates examples list from 'examples/' directory\n",
    "                    examples=example_list,\n",
    "                    title=title,\n",
    "                    description=description,\n",
    "                    article=article)\n",
    "\n",
    "# Launch demo\n",
    "\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "03owvJqAKGga"
   },
   "source": [
    "## Creating a requirements file for FoddVision Mini (`requirements.txt`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tAXl5Yt-Ly6T",
    "outputId": "08ef2d43-d95e-4f09-af76-37ce59a9ca97"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_mini/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_mini/requirements.txt\n",
    "torch==1.12.0\n",
    "torchvision==0.13.0\n",
    "gradio==3.1.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SCKuJjZtMFzO"
   },
   "source": [
    "# Deploying FoodVision Mini app to HuggingFace Spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R20gmyi1MRdB",
    "outputId": "eb3cf672-0903-4267-9389-d90a076f0abe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n",
      "app.py\n",
      "examples\n",
      "model.py\n",
      "requirements.txt\n"
     ]
    }
   ],
   "source": [
    "!ls demos/foodvision_mini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q63DUPLR5xX6",
    "outputId": "97548302-dfeb-4ec6-8315-4b8074a5685c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: 09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth (deflated 8%)\n",
      "  adding: app.py (deflated 55%)\n",
      "  adding: examples/ (stored 0%)\n",
      "  adding: examples/2582289.jpg (deflated 17%)\n",
      "  adding: examples/3622237.jpg (deflated 0%)\n",
      "  adding: examples/592799.jpg (deflated 1%)\n",
      "  adding: model.py (deflated 46%)\n",
      "  adding: requirements.txt (deflated 4%)\n"
     ]
    }
   ],
   "source": [
    "!cd demos/foodvision_mini && zip -r ../foodvision_mini.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "t6E0OPTC7xqc"
   },
   "outputs": [],
   "source": [
    "# Download the zipped FoodVision Mini app\n",
    "# try:\n",
    "#   from google.colab import files \n",
    "#   files.download('demos/foodvision_mini.zip')\n",
    "# except:\n",
    "#   print(\"Not running in Google Colab, can't use google. colab.files.download(), please manually download\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CkxAW8Pn8WK8"
   },
   "source": [
    "## Uploading to Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 770
    },
    "id": "LAYMXcNoqhL0",
    "outputId": "2a8fbb2b-2d59-4119-b32b-373536f712c7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"750\"\n",
       "            src=\"https://hf.space/embed/Azamat0315277/foodvision_mini/+\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7efad063e700>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "\n",
    "# Embed FoodVision Mini Gradio demo\n",
    "IFrame(src=\"https://hf.space/embed/Azamat0315277/foodvision_mini/+\", width=900, height=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nPiWObas8fhw"
   },
   "source": [
    "# Creating FoodVision Big"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mUqtnXm6tbHV"
   },
   "source": [
    "## Creating a model and transforms for FoodVision Big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85,
     "referenced_widgets": [
      "061b7d9daa914f368a1f29080bab2c50",
      "6683653246c44456bb38e0c76de81bde",
      "c6f5d5e24d364a568691c2ed1e58f937",
      "c4d455ca89c542cc89397337a4854fb7",
      "b8599b06bfa545a49b0bb1519a5993ab",
      "6add88bdd497469c9a2d60f3db8fb2aa",
      "28fe8b79aa9545449d3e0f0b181a5906",
      "66d295efd5c846b2a7939cd44d6bf1a5",
      "e16ed416ca6a41b381db2c46c6fa0241",
      "29d80cd9180b42579bf74f971e4a87c6",
      "b279d86f5cf844119a3a8e139fdb0d1b"
     ]
    },
    "id": "jm2UG7DAtmsU",
    "outputId": "e299475e-2fd5-44b8-c6d8-9e5d28cf8dfe"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b2_rwightman-bcdf34b7.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "061b7d9daa914f368a1f29080bab2c50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/35.2M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effnetb2_food101, effnetb2_transforms = create_effnetb2_model(num_classes=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "UKEvzHncu8BO"
   },
   "outputs": [],
   "source": [
    "#  Create Food101 training data transforms (only perform data augmentation on train set)\n",
    "food101_train_transforms = torchvision.transforms.Compose([\n",
    "      torchvision.transforms.TrivialAugmentWide(),\n",
    "      effnetb2_transforms\n",
    "])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "euum4CR4vwNS"
   },
   "source": [
    "## Getting data for FoodVision Big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 82,
     "referenced_widgets": [
      "c772b0022740481f96b504550cf4eabb",
      "bd0f032c10fb465698a8172cb1bc557e",
      "af1a55b9155a4cd18c390b00c8f7bdff",
      "37d2b13aa2f54480bb36acb4452a278b",
      "40e2f2b1a43541d881f8f8513a30e36f",
      "8f88d27791ac47d4919eb2e6e2111de0",
      "0355bae6b158437cbe40b78da5a1528e",
      "8cd38096b8f640d4ba0d70746b5d2920",
      "81b2279d799b4a889abe1576c6e7cf26",
      "c101511024424b9db79c7f9be9123189",
      "3fdf876125414dfa8651ad804d2ad97f"
     ]
    },
    "id": "OCMK8ei4v7qi",
    "outputId": "02b5ac4a-2d49-44e9-ab56-5362ebb88ad8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://data.vision.ee.ethz.ch/cvl/food-101.tar.gz to data/food-101.tar.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c772b0022740481f96b504550cf4eabb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4996278331 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/food-101.tar.gz to data\n"
     ]
    }
   ],
   "source": [
    "from torchvision import datasets\n",
    "\n",
    "# Setup data directory\n",
    "\n",
    "from pathlib import Path\n",
    "data_dir = Path('data')\n",
    "\n",
    "# Get training data\n",
    "train_data = datasets.Food101(root=data_dir,\n",
    "                               split='train',\n",
    "                               transform=food101_train_transforms,\n",
    "                               download=True)\n",
    "\n",
    "test_data = datasets.Food101(root=data_dir,\n",
    "                             split='test',\n",
    "                             transform=effnetb2_transforms,\n",
    "                             download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xClU8FlFw_Ie",
    "outputId": "70040c35-114f-4340-91ae-a6fa64bfe79d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple_pie',\n",
       " 'baby_back_ribs',\n",
       " 'baklava',\n",
       " 'beef_carpaccio',\n",
       " 'beef_tartare',\n",
       " 'beet_salad',\n",
       " 'beignets',\n",
       " 'bibimbap',\n",
       " 'bread_pudding',\n",
       " 'breakfast_burrito']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Food101 class names\n",
    "food101_class_names = train_data.classes\n",
    "\n",
    "food101_class_names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eRnCkKFuzQgB"
   },
   "source": [
    "## Creating a subset of the Food dataset for faster experimenting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U8d57oylzcow"
   },
   "source": [
    "## Turning FoodVision datasets into `Dataloader`s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G0vVvvMizza0",
    "outputId": "d296b509-7d7e-4aa5-a229-2d86ad63116f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75750, 25250)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data), len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N9xek6UNz6Ho"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch \n",
    "\n",
    "BATCH_SIZE = 32\n",
    "NUM_WORKERS = 2 if os.cpu_count() <= 4 else 4\n",
    "\n",
    "# Create Dataloaders\n",
    "train_dataloader_food101 = torch.utils.data.DataLoader(train_data,\n",
    "                                                       batch_size=BATCH_SIZE,\n",
    "                                                       shuffle=True,\n",
    "                                                       num_workers=NUM_WORKERS)\n",
    "\n",
    "test_dataloader_food101 = torch.utils.data.DataLoader(test_data,\n",
    "                                                      batch_size=BATCH_SIZE,\n",
    "                                                      shuffle=False,\n",
    "                                                      num_workers=NUM_WORKERS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3TwCHxpa3d2c"
   },
   "source": [
    "## Training FoodVision Big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 131,
     "referenced_widgets": [
      "78d8f35a0fc6440ea78a995dca8dff03",
      "bddd9aaa64e746d6806b64ea7c2ef683",
      "8a0e4224658549958bca381a03d5785c",
      "7a18da150c8e41508901149ffb9ab28d",
      "62cf09b44730442fa62f3a4624c503a3",
      "e8a2b8c569ec4579ac571a4430922a41",
      "e6a53143776245348738979e506d9651",
      "e3ae65dfd2e04342a07750867e3e3ef4",
      "682fa9fb4f084cbc9aa4380313e882d4",
      "02014f14d41e4f4a8b481d72b8a28c50",
      "72226df2fdbd439694648e4c341e926b"
     ]
    },
    "id": "ci0ORKUt3p55",
    "outputId": "0a37742f-d4d4-4b07-c46f-70dec82e5587"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78d8f35a0fc6440ea78a995dca8dff03",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 2.3898 | train_acc: 0.5389 | test_loss: 1.6083 | test_acc: 0.7668\n",
      "Epoch: 2 | train_loss: 1.9194 | train_acc: 0.6716 | test_loss: 1.4961 | test_acc: 0.7953\n",
      "Epoch: 3 | train_loss: 1.7759 | train_acc: 0.7124 | test_loss: 1.4469 | test_acc: 0.8108\n",
      "Epoch: 4 | train_loss: 1.6778 | train_acc: 0.7443 | test_loss: 1.3933 | test_acc: 0.8212\n",
      "Epoch: 5 | train_loss: 1.6036 | train_acc: 0.7636 | test_loss: 1.3661 | test_acc: 0.8308\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine \n",
    "\n",
    "# Setup optimizer \n",
    "optimizer = torch.optim.Adam(params=effnetb2_food101.parameters(),\n",
    "                             lr=0.001)\n",
    "\n",
    "# Setup loss function\n",
    "loss_fn = torch.nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "\n",
    "set_seeds()\n",
    "effnetb2_food101_results = engine.train(model=effnetb2_food101,\n",
    "                                        train_dataloader=train_dataloader_food101,\n",
    "                                        test_dataloader= test_dataloader_food101,\n",
    "                                        optimizer=optimizer,\n",
    "                                        loss_fn=loss_fn,\n",
    "                                        epochs=5,\n",
    "                                        device=device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fUP0eBDw5RNe"
   },
   "source": [
    "## Inspecting loss curves of FoodVision Big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "cm6Nf-e_Cw5j",
    "outputId": "1dde59dc-6c65-40dd-8cd4-510e532c798d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(effnetb2_food101_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_62hqy3YDPwM"
   },
   "source": [
    "## Saving and loading FoodVision Big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f8NbHQmgDdeX",
    "outputId": "5ee5f334-0154-4963-9c31-1afc06b9449a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: /content/drive/MyDrive/Models/pretrained_effnetb2_feature_extractor_food101_all.pth\n"
     ]
    }
   ],
   "source": [
    "from going_modular import utils\n",
    "\n",
    "# Create a model path\n",
    "effnetb2_food101_model_path = \"/content/drive/MyDrive/Models/pretrained_effnetb2_feature_extractor_food101_all.pth\" \n",
    "\n",
    "# Save model\n",
    "utils.save_model(model=effnetb2_food101,\n",
    "                 target_dir='models',\n",
    "                 model_name=effnetb2_food101_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102,
     "referenced_widgets": [
      "711de7ea4e4141b59af727814ffbfa13",
      "79492502979940429616ff67f2e291f7",
      "a8c032487e5a47f8aaf511e1414b8a57",
      "5759dbe9a06746178783ad829b1f612d",
      "89c22800668a41558d3ce73c0f9d5d1e",
      "ace47e5aa3024e01825868e222b0438e",
      "6832815d2f3546a5aeaacd00c3acfe66",
      "3501ac189206425c933d2497c75ac49d",
      "089d0d63eb094f0b8a34fd6991602c06",
      "c904c129a8144374b2f7e239f83365a7",
      "9de8ff2b8eb343ac9cc8380d700f02ac"
     ]
    },
    "id": "J3jT7Gt2H-4M",
    "outputId": "ac115dc2-b53b-4be3-d83a-543b968c8178"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b2_rwightman-bcdf34b7.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "711de7ea4e4141b59af727814ffbfa13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/35.2M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Food101 compatible EffNetB2 instance\n",
    "loaded_effnetb2_food101, effnetb2_transforms = create_effnetb2_model(num_classes=101)\n",
    "\n",
    "# Load model\n",
    "loaded_effnetb2_food101.load_state_dict(torch.load('/content/drive/MyDrive/Models/pretrained_effnetb2_feature_extractor_food101_all.pth'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7WgQsIlnZWZ0"
   },
   "source": [
    "## Checking FoodVision Bid model size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xx7pyieLZcei",
    "outputId": "3e328e14-2a4d-4045-ee60-a5a38b28933b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prefetched EffnetB2 Food101 model size: 30 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "pretrained_effnetb2_food101_model_size = Path(\"/content/drive/MyDrive/Models\", 'pretrained_effnetb2_feature_extractor_food101_all.pth').stat().st_size // (1024*1024)\n",
    "print(f\"Prefetched EffnetB2 Food101 model size: {pretrained_effnetb2_food101_model_size} MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ohSzvtSnakzT"
   },
   "source": [
    "# Turning FoodVision Big model into a deployable app"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sY0Es75ua79C"
   },
   "source": [
    "Create directory with structure like below:\n",
    "\n",
    "```\n",
    "demos/\n",
    "  foodvision_big/\n",
    "    09_pretrained_effnetb2_feature_extractor_food101_all.pth\n",
    "    app.py\n",
    "    class_names.txt\n",
    "    examples/\n",
    "      example_1.jpg\n",
    "    model.py\n",
    "    requirements.txt\n",
    "```\n",
    "* `09_pretrained_effnetb2_feature_extractor_food101_all.pth` is trained Pytorch model file. \n",
    "* `app.py` contains FoodVision Big Gradio app. \n",
    "* `class_names.txt` contains all of the class names of FoodVision Big. \n",
    "* `examples/` contains example images to use with Gradio app. \n",
    "* `model.py` contains the model definition as well as any transforms assosiated wwith the model. \n",
    "* `requirements.txt` \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HGh50m21bYAr"
   },
   "outputs": [],
   "source": [
    "from pathlib import Path \n",
    "\n",
    "# Create FoodVision Big demo path\n",
    "foodvision_big_demo_path = Path(\"demos/foodvision_big/\")\n",
    "\n",
    "# Make directory\n",
    "foodvision_big_demo_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Make examples directory\n",
    "(foodvision_big_demo_path / 'examples').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KZro-kB6fwRf"
   },
   "source": [
    "## Downloading an example image and moving it to the `example` directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JwSpCitBiHtB",
    "outputId": "f8b76384-af48-4198-8c49-32b839e74dc3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-03-12 11:16:22--  https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg\n",
      "Resolving raw.github.com (raw.github.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
      "Connecting to raw.github.com (raw.github.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg [following]\n",
      "--2023-03-12 11:16:22--  https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1199845 (1.1M) [image/jpeg]\n",
      "Saving to: ‘marina-grynykha-FBgCgJhPO2I-unsplash.jpg’\n",
      "\n",
      "marina-grynykha-FBg 100%[===================>]   1.14M  --.-KB/s    in 0.05s   \n",
      "\n",
      "2023-03-12 11:16:23 (21.9 MB/s) - ‘marina-grynykha-FBgCgJhPO2I-unsplash.jpg’ saved [1199845/1199845]\n",
      "\n",
      "--2023-03-12 11:16:23--  https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg\n",
      "Resolving raw.github.com (raw.github.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
      "Connecting to raw.github.com (raw.github.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg [following]\n",
      "--2023-03-12 11:16:23--  https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3396992 (3.2M) [image/jpeg]\n",
      "Saving to: ‘shourav-sheikh-a66sGfOnnqQ-unsplash.jpg’\n",
      "\n",
      "shourav-sheikh-a66s 100%[===================>]   3.24M  --.-KB/s    in 0.07s   \n",
      "\n",
      "2023-03-12 11:16:23 (46.7 MB/s) - ‘shourav-sheikh-a66sGfOnnqQ-unsplash.jpg’ saved [3396992/3396992]\n",
      "\n",
      "--2023-03-12 11:16:24--  https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg\n",
      "Resolving raw.github.com (raw.github.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
      "Connecting to raw.github.com (raw.github.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg [following]\n",
      "--2023-03-12 11:16:24--  https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2412682 (2.3M) [image/jpeg]\n",
      "Saving to: ‘tim-toomey-pe9dvM1rQkM-unsplash.jpg’\n",
      "\n",
      "tim-toomey-pe9dvM1r 100%[===================>]   2.30M  --.-KB/s    in 0.07s   \n",
      "\n",
      "2023-03-12 11:16:24 (35.1 MB/s) - ‘tim-toomey-pe9dvM1rQkM-unsplash.jpg’ saved [2412682/2412682]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg\n",
    "!wget https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg\n",
    "!wget https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dlcaXKkEf9cX",
    "outputId": "1049d1c3-5b20-437b-90b6-6a2fa9ddd78a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-03-12 11:22:21--  https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg\n",
      "Resolving raw.github.com (raw.github.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.github.com (raw.github.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg [following]\n",
      "--2023-03-12 11:22:21--  https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1199845 (1.1M) [image/jpeg]\n",
      "Saving to: ‘marina-grynykha-FBgCgJhPO2I-unsplash.jpg.1’\n",
      "\n",
      "marina-grynykha-FBg 100%[===================>]   1.14M  --.-KB/s    in 0.06s   \n",
      "\n",
      "2023-03-12 11:22:22 (20.0 MB/s) - ‘marina-grynykha-FBgCgJhPO2I-unsplash.jpg.1’ saved [1199845/1199845]\n",
      "\n",
      "--2023-03-12 11:22:22--  https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg\n",
      "Resolving raw.github.com (raw.github.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.github.com (raw.github.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg [following]\n",
      "--2023-03-12 11:22:22--  https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3396992 (3.2M) [image/jpeg]\n",
      "Saving to: ‘shourav-sheikh-a66sGfOnnqQ-unsplash.jpg.1’\n",
      "\n",
      "shourav-sheikh-a66s 100%[===================>]   3.24M  --.-KB/s    in 0.07s   \n",
      "\n",
      "2023-03-12 11:22:22 (46.2 MB/s) - ‘shourav-sheikh-a66sGfOnnqQ-unsplash.jpg.1’ saved [3396992/3396992]\n",
      "\n",
      "--2023-03-12 11:22:22--  https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg\n",
      "Resolving raw.github.com (raw.github.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.github.com (raw.github.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg [following]\n",
      "--2023-03-12 11:22:22--  https://raw.githubusercontent.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2412682 (2.3M) [image/jpeg]\n",
      "Saving to: ‘tim-toomey-pe9dvM1rQkM-unsplash.jpg.1’\n",
      "\n",
      "tim-toomey-pe9dvM1r 100%[===================>]   2.30M  --.-KB/s    in 0.06s   \n",
      "\n",
      "2023-03-12 11:22:23 (37.8 MB/s) - ‘tim-toomey-pe9dvM1rQkM-unsplash.jpg.1’ saved [2412682/2412682]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get example images from github\n",
    "!wget https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/marina-grynykha-FBgCgJhPO2I-unsplash.jpg\n",
    "!wget https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg\n",
    "!wget https://raw.github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision/main/example/tim-toomey-pe9dvM1rQkM-unsplash.jpg\n",
    "# Move examples into example directory\n",
    "!mv /content/marina-grynykha-FBgCgJhPO2I-unsplash.jpg  /content/demos/foodvision_big/examples/marina-grynykha-FBgCgJhPO2I-unsplash.jpg\n",
    "!mv /content/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg /content/demos/foodvision_big/examples/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg\n",
    "!mv /content/tim-toomey-pe9dvM1rQkM-unsplash.jpg  /content/demos/foodvision_big/examples/tim-toomey-pe9dvM1rQkM-unsplash.jpg\n",
    "\n",
    "# Move trained model into Food101 Big demo folder\n",
    "!mv /content/drive/MyDrive/Models/pretrained_effnetb2_feature_extractor_food101_all.pth /content/demos/foodvision_big"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i0qgctd_jqRa"
   },
   "source": [
    "## Saving Food101 class names to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G0bkSmJKjyzS",
    "outputId": "b1fd7209-8666-4bca-f927-c84dbc63fcfb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving Food101 class names to demos/foodvision_big/class_names.txt\n"
     ]
    }
   ],
   "source": [
    "# Create path\n",
    "foodvision_big_class_names_path = foodvision_big_demo_path / 'class_names.txt'\n",
    "\n",
    "# Write Food101 class names list to file\n",
    "with open(foodvision_big_class_names_path, 'w') as f:\n",
    "  print(f\"[INFO] Saving Food101 class names to {foodvision_big_class_names_path}\")\n",
    "  f.write(\"\\n\".join(food101_class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I7W9fIOGmX-W",
    "outputId": "51b10103-e081-4ec2-971b-afbd1be76bce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check class names\n",
    "with open(foodvision_big_class_names_path, 'r') as f:\n",
    "  food101_class_names_loaded = [food.strip() for food in f.readlines()]\n",
    "\n",
    "# View the first 5 class names loaded back in\n",
    "food101_class_names_loaded[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pJ0XAcf5m0UR"
   },
   "source": [
    "## Create `model.py`\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wL1NAX9Em6yc",
    "outputId": "57679675-236e-4065-8b4c-ca015b86c40a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_big/model.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_big/model.py\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "\n",
    "def create_effnetb2_model(num_classes: int=3,seed: int=42):\n",
    "  \"\"\"\n",
    "  Creates an  EfficientNetB2 feature extractor model and transforms\n",
    "  \"\"\"\n",
    "  # Create EffNetB2 pretrained weights, transfroms and model\n",
    "  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "  transforms = weights.transform()\n",
    "  model = torchvision.models.efficientnet_b2(weights=weights)\n",
    "\n",
    "  # Freeze all layers in base model\n",
    "  for param in model.parameters():\n",
    "    param.requires_grad=False\n",
    "\n",
    "  # Change classifier head with random seed for reproducibility \n",
    "  torch.manual_seed(seed)\n",
    "  model.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.3, inplace=True),\n",
    "    nn.Linear(in_features=1408, out_features=num_classes)\n",
    "  )\n",
    "\n",
    "  return model, transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9EHog7ERnb9n"
   },
   "source": [
    "## Create `app.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DgAeoSSrnhyZ",
    "outputId": "74eafd1f-aaba-4277-eeab-40c4789c4115"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_big/app.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_big/app.py\n",
    "### 1. Imports and class names setup ### \n",
    "import gradio as gr\n",
    "import os\n",
    "import torch\n",
    "\n",
    "from model import create_effnetb2_model\n",
    "from timeit import default_timer as timer\n",
    "from typing import Tuple, Dict\n",
    "\n",
    "# Setup class names\n",
    "with open(\"class_names.txt\", \"r\") as f: # reading them in from class_names.txt\n",
    "    class_names = [food_name.strip() for food_name in  f.readlines()]\n",
    "    \n",
    "### 2. Model and transforms preparation ###    \n",
    "\n",
    "# Create model\n",
    "effnetb2, effnetb2_transforms = create_effnetb2_model(\n",
    "    num_classes=101\n",
    ")\n",
    "\n",
    "# Load saved weights\n",
    "effnetb2.load_state_dict(\n",
    "    torch.load(\n",
    "        f=\"pretrained_effnetb2_feature_extractor_food101_all.pth\",\n",
    "        map_location=torch.device(\"cpu\"),  # load to CPU\n",
    "    )\n",
    ")\n",
    "\n",
    "### 3. Predict function ###\n",
    "\n",
    "# Create predict function\n",
    "def predict(img) -> Tuple[Dict, float]:\n",
    "    \"\"\"Transforms and performs a prediction on img and returns prediction and time taken.\n",
    "    \"\"\"\n",
    "    # Start the timer\n",
    "    start_time = timer()\n",
    "    \n",
    "    # Transform the target image and add a batch dimension\n",
    "    img = effnetb2_transforms(img).unsqueeze(0)\n",
    "    \n",
    "    # Put model into evaluation mode and turn on inference mode\n",
    "    effnetb2.eval()\n",
    "    with torch.inference_mode():\n",
    "        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities\n",
    "        pred_probs = torch.softmax(effnetb2(img), dim=1)\n",
    "    \n",
    "    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)\n",
    "    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
    "    \n",
    "    # Calculate the prediction time\n",
    "    pred_time = round(timer() - start_time, 5)\n",
    "    \n",
    "    # Return the prediction dictionary and prediction time \n",
    "    return pred_labels_and_probs, pred_time\n",
    "\n",
    "### 4. Gradio app ###\n",
    "\n",
    "# Create title, description and article strings\n",
    "title = \"FoodVision Big 🍔👁\"\n",
    "description = \"An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes]\"\n",
    "article = \"Created at https://github.com/Azamat0315277/pytorch_paper_replicating_for_computer_vision\"\n",
    "\n",
    "# Create examples list from \"examples/\" directory\n",
    "example_list = [[\"examples/\" + example] for example in os.listdir(\"examples\")]\n",
    "\n",
    "# Create Gradio interface \n",
    "demo = gr.Interface(\n",
    "    fn=predict,\n",
    "    inputs=gr.Image(type=\"pil\"),\n",
    "    outputs=[\n",
    "        gr.Label(num_top_classes=5, label=\"Predictions\"),\n",
    "        gr.Number(label=\"Prediction time (s)\"),\n",
    "    ],\n",
    "    examples=example_list,\n",
    "    title=title,\n",
    "    description=description,\n",
    "    article=article,\n",
    ")\n",
    "\n",
    "# Launch the app!\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YlKrWMZ8sUB-"
   },
   "source": [
    "## Create a requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BQS4ojxpsg-e",
    "outputId": "9b92f773-581a-4a46-973e-450c27cbe962"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_big/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_big/requirements.txt\n",
    "torch==1.12.0\n",
    "torchvision==0.13.0\n",
    "gradio==3.1.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WxhrdztusoUr"
   },
   "source": [
    "## Downloading FoodVision Big app files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "id": "HUzUc27ZtEOt",
    "outputId": "a74388b2-1b72-44b9-c658-d716cc8e04bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: app.py (deflated 55%)\n",
      "  adding: class_names.txt (deflated 48%)\n",
      "  adding: examples/ (stored 0%)\n",
      "  adding: examples/tim-toomey-pe9dvM1rQkM-unsplash.jpg (deflated 1%)\n",
      "  adding: examples/marina-grynykha-FBgCgJhPO2I-unsplash.jpg (deflated 3%)\n",
      "  adding: examples/shourav-sheikh-a66sGfOnnqQ-unsplash.jpg (deflated 0%)\n",
      "  adding: model.py (deflated 46%)\n",
      "  adding: pretrained_effnetb2_feature_extractor_food101_all.pth (deflated 8%)\n",
      "  adding: requirements.txt (deflated 4%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "    async function download(id, filename, size) {\n",
       "      if (!google.colab.kernel.accessAllowed) {\n",
       "        return;\n",
       "      }\n",
       "      const div = document.createElement('div');\n",
       "      const label = document.createElement('label');\n",
       "      label.textContent = `Downloading \"${filename}\": `;\n",
       "      div.appendChild(label);\n",
       "      const progress = document.createElement('progress');\n",
       "      progress.max = size;\n",
       "      div.appendChild(progress);\n",
       "      document.body.appendChild(div);\n",
       "\n",
       "      const buffers = [];\n",
       "      let downloaded = 0;\n",
       "\n",
       "      const channel = await google.colab.kernel.comms.open(id);\n",
       "      // Send a message to notify the kernel that we're ready.\n",
       "      channel.send({})\n",
       "\n",
       "      for await (const message of channel.messages) {\n",
       "        // Send a message to notify the kernel that we're ready.\n",
       "        channel.send({})\n",
       "        if (message.buffers) {\n",
       "          for (const buffer of message.buffers) {\n",
       "            buffers.push(buffer);\n",
       "            downloaded += buffer.byteLength;\n",
       "            progress.value = downloaded;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
       "      const a = document.createElement('a');\n",
       "      a.href = window.URL.createObjectURL(blob);\n",
       "      a.download = filename;\n",
       "      div.appendChild(a);\n",
       "      a.click();\n",
       "      div.remove();\n",
       "    }\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "download(\"download_bc89c800-6a3d-411c-8b9d-7ad7d62f3019\", \"foodvision_big.zip\", 36405973)"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zip foodvision_big folder but exclude certain files\n",
    "!cd demos/foodvision_big && zip -r ../foodvision_big.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\"\n",
    "\n",
    "# Download the zipped FoodVision Big app (if running in Google Colab)\n",
    "try:\n",
    "    from google.colab import files\n",
    "    files.download(\"demos/foodvision_big.zip\")\n",
    "except:\n",
    "    print(\"Not running in Google Colab, can't use google.colab.files.download()\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aYK6dGZdtLC9"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyNTdCXRuTyiLtEF+hUoj4yw",
   "include_colab_link": true,
   "mount_file_id": "1ONqPCS0KMOgYFf69nseKtGHwix48-EBU",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "02014f14d41e4f4a8b481d72b8a28c50": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "0355bae6b158437cbe40b78da5a1528e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "04006c1e764442c7927270dca7108800": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "05f8e2eb04ee4302b9652d43fa1b3248": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "061b7d9daa914f368a1f29080bab2c50": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_6683653246c44456bb38e0c76de81bde",
       "IPY_MODEL_c6f5d5e24d364a568691c2ed1e58f937",
       "IPY_MODEL_c4d455ca89c542cc89397337a4854fb7"
      ],
      "layout": "IPY_MODEL_b8599b06bfa545a49b0bb1519a5993ab"
     }
    },
    "07fcc51306f24ec6ad11d42e9c26158b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_25d88390bdc94374b073f0ef1965deff",
      "placeholder": "​",
      "style": "IPY_MODEL_8c9bff84c57b468f8c0c2f7ede3bcf31",
      "value": "100%"
     }
    },
    "089d0d63eb094f0b8a34fd6991602c06": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "09d74e4e4e654ca5b1d8433ae0bb6e31": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "0b8b0d9f1af74901bcd9cc4fb1c7e6ca": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_801a1720b890414f9bc9e79632607e21",
      "max": 150,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_34e97ca277f7466a86468934d23ae437",
      "value": 150
     }
    },
    "0cda129e20c4440fba52fea4d3a44b16": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c01aa17ea24e48fc81f05e4cf6ca5ac4",
      "max": 346328529,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_e94960023c4448d5af9e2eaffb9c3874",
      "value": 346328529
     }
    },
    "0da1c1783ccd47e0a3f0ad471b602e12": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "13aec9269462480f9040dad1673efa9d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "17845d86c96c44f6b883fddbe5c1f133": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "17b794d99d5d4c618428f58814bb0ed7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_6837fca5db5240d899914b02a598269b",
      "max": 150,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_eedb9d4867404497b2dd13b5f2487158",
      "value": 150
     }
    },
    "1e7a027302fd4880b475ddf025bf9560": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_20802249c3e5499e8711ee95ad993880",
      "placeholder": "​",
      "style": "IPY_MODEL_62d6bdda54b74449bdf62b17fcad9313",
      "value": " 150/150 [00:16&lt;00:00,  9.84it/s]"
     }
    },
    "20802249c3e5499e8711ee95ad993880": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "25568a2ec025423083be8489bcdbbbd9": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "25d88390bdc94374b073f0ef1965deff": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "28fe8b79aa9545449d3e0f0b181a5906": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "29d80cd9180b42579bf74f971e4a87c6": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "2f257d23b23f408d8cba39c81dad14a9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_32f79ce465f7493c94015aaf190e0526",
      "placeholder": "​",
      "style": "IPY_MODEL_a05df77632f340699514f6d13103ad83",
      "value": " 35.2M/35.2M [00:00&lt;00:00, 83.2MB/s]"
     }
    },
    "32f79ce465f7493c94015aaf190e0526": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "3309ebd093dd44bdab978580f7cc891c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "33c6e17ddb83403bb4241f66d4196f33": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "34e97ca277f7466a86468934d23ae437": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "3501ac189206425c933d2497c75ac49d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "37d2b13aa2f54480bb36acb4452a278b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c101511024424b9db79c7f9be9123189",
      "placeholder": "​",
      "style": "IPY_MODEL_3fdf876125414dfa8651ad804d2ad97f",
      "value": " 4996278331/4996278331 [05:25&lt;00:00, 16049788.88it/s]"
     }
    },
    "388dabafe3dd466c85f3fd892222fa5a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "3bc12f0a5764419a85b1f462b56458e9": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "3fdf876125414dfa8651ad804d2ad97f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "40e2f2b1a43541d881f8f8513a30e36f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "461343d7fecb4f22945e91c4cd9ddd91": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "47805df7930e4bb596ee76c826a9ca39": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_3bc12f0a5764419a85b1f462b56458e9",
      "max": 20,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_b0cb3e99d3f7495d8d70df54dc2bead1",
      "value": 20
     }
    },
    "47df6ea5a175421180aa750656fbab9d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "4c232c0d99cc4b9f916797a8e622a809": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_6f900a18588547f89587f61e6c833575",
       "IPY_MODEL_0cda129e20c4440fba52fea4d3a44b16",
       "IPY_MODEL_7e04ee6b0e6a452b99d72a3dd6cae451"
      ],
      "layout": "IPY_MODEL_13aec9269462480f9040dad1673efa9d"
     }
    },
    "56b3fdca67f24056a3f0671dfb154f51": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b01af292bf584575b9b515bac6d0903a",
      "max": 36882185,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_82ad31b7f1604eb385b91c37ea181fee",
      "value": 36882185
     }
    },
    "57166e859d654b02b3bbeea921205afe": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_dd0b79887a714cf3b8683325eb6e4941",
      "placeholder": "​",
      "style": "IPY_MODEL_09d74e4e4e654ca5b1d8433ae0bb6e31",
      "value": "100%"
     }
    },
    "5759dbe9a06746178783ad829b1f612d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c904c129a8144374b2f7e239f83365a7",
      "placeholder": "​",
      "style": "IPY_MODEL_9de8ff2b8eb343ac9cc8380d700f02ac",
      "value": " 35.2M/35.2M [00:00&lt;00:00, 89.4MB/s]"
     }
    },
    "62cf09b44730442fa62f3a4624c503a3": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "62d6bdda54b74449bdf62b17fcad9313": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "62dd84b6a5e94ea5b101ba1af433784e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "6683653246c44456bb38e0c76de81bde": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_6add88bdd497469c9a2d60f3db8fb2aa",
      "placeholder": "​",
      "style": "IPY_MODEL_28fe8b79aa9545449d3e0f0b181a5906",
      "value": "100%"
     }
    },
    "66d295efd5c846b2a7939cd44d6bf1a5": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "682fa9fb4f084cbc9aa4380313e882d4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "6832815d2f3546a5aeaacd00c3acfe66": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "6837fca5db5240d899914b02a598269b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6add88bdd497469c9a2d60f3db8fb2aa": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6f900a18588547f89587f61e6c833575": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_0da1c1783ccd47e0a3f0ad471b602e12",
      "placeholder": "​",
      "style": "IPY_MODEL_3309ebd093dd44bdab978580f7cc891c",
      "value": "100%"
     }
    },
    "711de7ea4e4141b59af727814ffbfa13": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_79492502979940429616ff67f2e291f7",
       "IPY_MODEL_a8c032487e5a47f8aaf511e1414b8a57",
       "IPY_MODEL_5759dbe9a06746178783ad829b1f612d"
      ],
      "layout": "IPY_MODEL_89c22800668a41558d3ce73c0f9d5d1e"
     }
    },
    "72226df2fdbd439694648e4c341e926b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "78d8f35a0fc6440ea78a995dca8dff03": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_bddd9aaa64e746d6806b64ea7c2ef683",
       "IPY_MODEL_8a0e4224658549958bca381a03d5785c",
       "IPY_MODEL_7a18da150c8e41508901149ffb9ab28d"
      ],
      "layout": "IPY_MODEL_62cf09b44730442fa62f3a4624c503a3"
     }
    },
    "79492502979940429616ff67f2e291f7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ace47e5aa3024e01825868e222b0438e",
      "placeholder": "​",
      "style": "IPY_MODEL_6832815d2f3546a5aeaacd00c3acfe66",
      "value": "100%"
     }
    },
    "7a18da150c8e41508901149ffb9ab28d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_02014f14d41e4f4a8b481d72b8a28c50",
      "placeholder": "​",
      "style": "IPY_MODEL_72226df2fdbd439694648e4c341e926b",
      "value": " 5/5 [2:00:47&lt;00:00, 1433.03s/it]"
     }
    },
    "7a6915aa66c34e62bfad8915eb3a4080": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7ac09671ee354e2f94c5e737c96a7328": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_05f8e2eb04ee4302b9652d43fa1b3248",
      "max": 20,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_99eccc0618f949caaf82058268bae425",
      "value": 20
     }
    },
    "7e04ee6b0e6a452b99d72a3dd6cae451": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_cb447be6ae5b417e8e3fc33cab459dab",
      "placeholder": "​",
      "style": "IPY_MODEL_62dd84b6a5e94ea5b101ba1af433784e",
      "value": " 330M/330M [00:03&lt;00:00, 93.3MB/s]"
     }
    },
    "801a1720b890414f9bc9e79632607e21": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "81b2279d799b4a889abe1576c6e7cf26": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "81bccdc7977e4b658418242ce507c49a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "82232cfbfd924b948e6317c1ec567477": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_07fcc51306f24ec6ad11d42e9c26158b",
       "IPY_MODEL_56b3fdca67f24056a3f0671dfb154f51",
       "IPY_MODEL_2f257d23b23f408d8cba39c81dad14a9"
      ],
      "layout": "IPY_MODEL_7a6915aa66c34e62bfad8915eb3a4080"
     }
    },
    "82ad31b7f1604eb385b91c37ea181fee": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "8374c645d1e84187be3b836eb8bfbb7a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_dfa9c86245744629889ed3a79c5cc488",
       "IPY_MODEL_17b794d99d5d4c618428f58814bb0ed7",
       "IPY_MODEL_9e488c65f67f41a29b4ab1ac2acf4940"
      ],
      "layout": "IPY_MODEL_33c6e17ddb83403bb4241f66d4196f33"
     }
    },
    "84b13ae6858c46acac57fba8f06f2b72": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_461343d7fecb4f22945e91c4cd9ddd91",
      "placeholder": "​",
      "style": "IPY_MODEL_388dabafe3dd466c85f3fd892222fa5a",
      "value": " 20/20 [02:59&lt;00:00,  8.75s/it]"
     }
    },
    "864dddefe5594c51b38e9dcf31ce7c8a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_f761e37a08d148a9b346c942ac368041",
      "placeholder": "​",
      "style": "IPY_MODEL_dffad845e7a3474095bc6bd50ac68966",
      "value": "100%"
     }
    },
    "89c22800668a41558d3ce73c0f9d5d1e": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8a0e4224658549958bca381a03d5785c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e3ae65dfd2e04342a07750867e3e3ef4",
      "max": 5,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_682fa9fb4f084cbc9aa4380313e882d4",
      "value": 5
     }
    },
    "8b5b5ff96b784a5587dc3ef0f3642170": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_fdf42b71dfa742bcbdf28367cb490407",
       "IPY_MODEL_0b8b0d9f1af74901bcd9cc4fb1c7e6ca",
       "IPY_MODEL_1e7a027302fd4880b475ddf025bf9560"
      ],
      "layout": "IPY_MODEL_81bccdc7977e4b658418242ce507c49a"
     }
    },
    "8c9bff84c57b468f8c0c2f7ede3bcf31": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "8cd38096b8f640d4ba0d70746b5d2920": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8f88d27791ac47d4919eb2e6e2111de0": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "99eccc0618f949caaf82058268bae425": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "9de8ff2b8eb343ac9cc8380d700f02ac": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "9e488c65f67f41a29b4ab1ac2acf4940": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_04006c1e764442c7927270dca7108800",
      "placeholder": "​",
      "style": "IPY_MODEL_f891f9c1064f4d9da7ad730eb307fa52",
      "value": " 150/150 [01:32&lt;00:00,  1.87it/s]"
     }
    },
    "a05df77632f340699514f6d13103ad83": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "a8c032487e5a47f8aaf511e1414b8a57": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_3501ac189206425c933d2497c75ac49d",
      "max": 36882185,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_089d0d63eb094f0b8a34fd6991602c06",
      "value": 36882185
     }
    },
    "ace47e5aa3024e01825868e222b0438e": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "af1a55b9155a4cd18c390b00c8f7bdff": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8cd38096b8f640d4ba0d70746b5d2920",
      "max": 4996278331,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_81b2279d799b4a889abe1576c6e7cf26",
      "value": 4996278331
     }
    },
    "b01af292bf584575b9b515bac6d0903a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b0cb3e99d3f7495d8d70df54dc2bead1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "b1b5315750ec4163bfbb09db59b0d51c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_57166e859d654b02b3bbeea921205afe",
       "IPY_MODEL_47805df7930e4bb596ee76c826a9ca39",
       "IPY_MODEL_efc7dd88fca749188a5919050cd559aa"
      ],
      "layout": "IPY_MODEL_b74b60a8244345ab99439bba14a8f6fe"
     }
    },
    "b279d86f5cf844119a3a8e139fdb0d1b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b74b60a8244345ab99439bba14a8f6fe": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b8599b06bfa545a49b0bb1519a5993ab": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "bd0f032c10fb465698a8172cb1bc557e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8f88d27791ac47d4919eb2e6e2111de0",
      "placeholder": "​",
      "style": "IPY_MODEL_0355bae6b158437cbe40b78da5a1528e",
      "value": "100%"
     }
    },
    "bddd9aaa64e746d6806b64ea7c2ef683": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e8a2b8c569ec4579ac571a4430922a41",
      "placeholder": "​",
      "style": "IPY_MODEL_e6a53143776245348738979e506d9651",
      "value": "100%"
     }
    },
    "c01aa17ea24e48fc81f05e4cf6ca5ac4": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c101511024424b9db79c7f9be9123189": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c230d4cbe47c4fac8a9181897df86a37": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c4d455ca89c542cc89397337a4854fb7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_29d80cd9180b42579bf74f971e4a87c6",
      "placeholder": "​",
      "style": "IPY_MODEL_b279d86f5cf844119a3a8e139fdb0d1b",
      "value": " 35.2M/35.2M [00:00&lt;00:00, 85.0MB/s]"
     }
    },
    "c6f5d5e24d364a568691c2ed1e58f937": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_66d295efd5c846b2a7939cd44d6bf1a5",
      "max": 36882185,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_e16ed416ca6a41b381db2c46c6fa0241",
      "value": 36882185
     }
    },
    "c772b0022740481f96b504550cf4eabb": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_bd0f032c10fb465698a8172cb1bc557e",
       "IPY_MODEL_af1a55b9155a4cd18c390b00c8f7bdff",
       "IPY_MODEL_37d2b13aa2f54480bb36acb4452a278b"
      ],
      "layout": "IPY_MODEL_40e2f2b1a43541d881f8f8513a30e36f"
     }
    },
    "c904c129a8144374b2f7e239f83365a7": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "cb447be6ae5b417e8e3fc33cab459dab": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d2aff66eafd64e3daa90106f43d5f7d2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "dd0b79887a714cf3b8683325eb6e4941": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "dfa9c86245744629889ed3a79c5cc488": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_17845d86c96c44f6b883fddbe5c1f133",
      "placeholder": "​",
      "style": "IPY_MODEL_f9fffe83602b42148e53e11abc79cdbc",
      "value": "100%"
     }
    },
    "dffad845e7a3474095bc6bd50ac68966": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e09a4737b4504235a668238b1b1257af": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e0ff2912385146e294f22f0727145ae7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_864dddefe5594c51b38e9dcf31ce7c8a",
       "IPY_MODEL_7ac09671ee354e2f94c5e737c96a7328",
       "IPY_MODEL_84b13ae6858c46acac57fba8f06f2b72"
      ],
      "layout": "IPY_MODEL_e09a4737b4504235a668238b1b1257af"
     }
    },
    "e16ed416ca6a41b381db2c46c6fa0241": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "e3ae65dfd2e04342a07750867e3e3ef4": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e6a53143776245348738979e506d9651": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e8a2b8c569ec4579ac571a4430922a41": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e94960023c4448d5af9e2eaffb9c3874": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "eedb9d4867404497b2dd13b5f2487158": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "efc7dd88fca749188a5919050cd559aa": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_25568a2ec025423083be8489bcdbbbd9",
      "placeholder": "​",
      "style": "IPY_MODEL_47df6ea5a175421180aa750656fbab9d",
      "value": " 20/20 [02:40&lt;00:00,  8.02s/it]"
     }
    },
    "f761e37a08d148a9b346c942ac368041": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f891f9c1064f4d9da7ad730eb307fa52": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "f9fffe83602b42148e53e11abc79cdbc": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "fdf42b71dfa742bcbdf28367cb490407": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c230d4cbe47c4fac8a9181897df86a37",
      "placeholder": "​",
      "style": "IPY_MODEL_d2aff66eafd64e3daa90106f43d5f7d2",
      "value": "100%"
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}