DINOV2.ipynb

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        "<a href=\"https://colab.research.google.com/github/AriPathak/Flowers102-TPU/blob/main/DINOV2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Collecting vit_pytorch\n",
            "  Downloading vit_pytorch-1.6.5-py3-none-any.whl (100 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.3/100.3 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting einops>=0.7.0 (from vit_pytorch)\n",
            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=1.10 in /usr/local/lib/python3.10/dist-packages (from vit_pytorch) (2.1.0+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from vit_pytorch) (0.16.0+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10->vit_pytorch) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10->vit_pytorch) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10->vit_pytorch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10->vit_pytorch) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10->vit_pytorch) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10->vit_pytorch) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10->vit_pytorch) (2.1.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision->vit_pytorch) (1.25.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision->vit_pytorch) (2.31.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->vit_pytorch) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10->vit_pytorch) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->vit_pytorch) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->vit_pytorch) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->vit_pytorch) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->vit_pytorch) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10->vit_pytorch) (1.3.0)\n",
            "Installing collected packages: einops, vit_pytorch\n",
            "Successfully installed einops-0.7.0 vit_pytorch-1.6.5\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "!pip install vit_pytorch\n",
        "from vit_pytorch import ViT, Dino\n",
        "import torchvision\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision.transforms import ToTensor\n",
        "from skimage import io\n",
        "from torchvision import datasets, transforms, models\n",
        "import tarfile\n",
        "from torchvision.datasets.utils import download_url\n",
        "from torch.utils.data import random_split, TensorDataset, Dataset\n",
        "import torch.nn as nn\n",
        "import seaborn as sns\n",
        "from PIL import Image\n",
        "from collections import OrderedDict\n",
        "from torch import optim\n",
        "from torch.optim import lr_scheduler\n",
        "from torch.autograd import Variable\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')\n",
        "test_img = torch.randn(1, 3, 224, 224)\n",
        "dinov2_vits14(test_img).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njgub1AJ_IAM",
        "outputId": "46d75b4a-dc52-44fa-bc3d-d851e16ce93f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/facebookresearch/dinov2/zipball/main\" to /root/.cache/torch/hub/main.zip\n",
            "/root/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/swiglu_ffn.py:51: UserWarning: xFormers is not available (SwiGLU)\n",
            "  warnings.warn(\"xFormers is not available (SwiGLU)\")\n",
            "/root/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/attention.py:33: UserWarning: xFormers is not available (Attention)\n",
            "  warnings.warn(\"xFormers is not available (Attention)\")\n",
            "/root/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/block.py:40: UserWarning: xFormers is not available (Block)\n",
            "  warnings.warn(\"xFormers is not available (Block)\")\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth\" to /root/.cache/torch/hub/checkpoints/dinov2_vits14_pretrain.pth\n",
            "100%|██████████| 84.2M/84.2M [00:00<00:00, 190MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 384])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for name, layer in dinov2_vits14.named_modules():\n",
        "  #print(layer)\n",
        "\n",
        "learnable_modules = ['blocks.8',\n",
        "                    'blocks.9',\n",
        "                    'blocks.10',\n",
        "                    'blocks.11']\n",
        "dinov2_vits14.patch_embed.proj = nn.Conv2d(4, 384, kernel_size=(14, 14), stride=(14, 14))\n",
        "dinov2_vits14.requires_grad_(False)\n",
        "modules = dict(dinov2_vits14.named_modules())\n",
        "for name in learnable_modules:\n",
        "    modules[name].requires_grad_(True)"
      ],
      "metadata": {
        "id": "3cpMml44KyVN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class L2Norm(nn.Module):\n",
        "    def forward(self, x, eps = 1e-6):\n",
        "        norm = x.norm(dim = 1, keepdim = True).clamp(min = eps)\n",
        "        return x / norm\n",
        "\n",
        "class MLP(nn.Module):\n",
        "  def __init__(self, dim, num_classes):\n",
        "    super().__init__()\n",
        "    self.layer = nn.Linear(dim, num_classes)\n",
        "  def forward(self, x):\n",
        "    return self.layer(x)"
      ],
      "metadata": {
        "id": "W1kTEQIKBB_6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_url = \"https://s3.amazonaws.com/fast-ai-imageclas/oxford-102-flowers.tgz\"\n",
        "download_url(dataset_url, '.')\n",
        "\n",
        "with tarfile.open('./oxford-102-flowers.tgz', 'r:gz') as tar:\n",
        "    tar.extractall(path='./data')\n",
        "\n",
        "img_path='./data/oxford-102-flowers/jpg/image_00001.jpg'\n",
        "\n",
        "class flowersmodel(Dataset):\n",
        "  def __init__(self,excel_file,root_dir,transform=None):\n",
        "    self.annotations=pd. read_csv(excel_file,delimiter=' ')\n",
        "    self.root_dir=root_dir\n",
        "    self.transform=transform\n",
        "  def __len__(self):\n",
        "    return len(self.annotations)\n",
        "  def __getitem__(self,index):\n",
        "    img_path=os.path.join(self.root_dir,self.annotations.iloc[index,0])\n",
        "    image=io.imread(img_path)\n",
        "    y_label=torch.tensor(self.annotations.iloc[index,1])\n",
        "    image=Image.open(img_path).resize((300,300),resample=0)\n",
        "    if self.transform:\n",
        "      image=self.transform(image)\n",
        "    return (image,y_label)\n",
        "\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomRotation(75),\n",
        "        transforms.Resize(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'validation': transforms.Compose([\n",
        "        transforms.Resize(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'testing': transforms.Compose([\n",
        "        transforms.Resize(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "}\n",
        "\n",
        "\n",
        "train_dataset=flowersmodel('./data/oxford-102-flowers/train.txt',root_dir='./data/oxford-102-flowers',transform=data_transforms['train'])\n",
        "test_dataset=flowersmodel('./data/oxford-102-flowers/test.txt',root_dir='./data/oxford-102-flowers',transform=data_transforms['testing'])\n",
        "val_dataset=flowersmodel('./data/oxford-102-flowers/valid.txt',root_dir='./data/oxford-102-flowers',transform=data_transforms['validation'])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fxQEYE0XlZER",
        "outputId": "d2ae5687-e543-4841-be60-85704b3b2d15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using downloaded and verified file: ./oxford-102-flowers.tgz\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataloaders = {\n",
        "    'training' : torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True),\n",
        "    'testing' : torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False),\n",
        "    'validation' : torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=True)\n",
        "}"
      ],
      "metadata": {
        "id": "27xiZmwmlyUx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def display(inp):\n",
        "  B, C, H, W = inp.shape\n",
        "  if (C != 3):\n",
        "    inp = inp.permute(0, 3, 2, 1)\n",
        "  plt.imshow(inp)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "xFUj1Cd8ZqSo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ViT_Dino(nn.Module):\n",
        "  def __init__(self, num_classes, in_feats=384):\n",
        "    super().__init__()\n",
        "    self.encoder = dinov2_vits14\n",
        "    self.classifier = MLP(in_feats, num_classes)\n",
        "  def forward(self, x):\n",
        "    #x = L2Norm(x)\n",
        "    x = self.encoder(x)\n",
        "    return self.classifier(x)\n"
      ],
      "metadata": {
        "id": "J7vtkfDSaGjX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.notebook import tqdm\n",
        "import time\n",
        "import os\n",
        "device = \"cuda:0\"\n",
        "def pretrain(model, learner, train_loader, optimizer, epochs, PATH=None):\n",
        "  model.to(device)\n",
        "  model.train()\n",
        "  for epoch in tqdm(range(1, epochs + 1)):\n",
        "    check = 0\n",
        "    for images, labels in tqdm(train_loader, leave=False):\n",
        "      images, labels = images.to(device), labels.to(device)\n",
        "      optimizer.zero_grad()\n",
        "      loss = learner(images)\n",
        "      avg_loss += loss\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "      learner.update_moving_average()\n",
        "      if check % 100 == 0:\n",
        "        print(loss.detach())\n",
        "    check += 1\n",
        "  if PATH:\n",
        "    torch.save(model.state_dict(), PATH)\n",
        "\n",
        "def compute_accuracy(model, loader):\n",
        "    total_correct = 0\n",
        "\n",
        "    model.cuda()\n",
        "    model.eval()\n",
        "    for inputs, labels in tqdm(loader, leave=False):\n",
        "        inputs, labels = inputs.cuda(), labels.cuda()\n",
        "        output = model(inputs)\n",
        "        _, pred = torch.max(output, 1)\n",
        "        for d in zip(pred, labels):\n",
        "          if d[0].item() == d[1].item():\n",
        "            total_correct += 1\n",
        "    return total_correct / len(loader.dataset)\n",
        "\n",
        "def finetune(model, train_loader, val_loader, num_epochs, criterion, optimizer, path=None, scheduler=None, pretrained=False, pretrained_path=None):\n",
        "    print('beginning to train model')\n",
        "    if path and not os.path.exists(path):\n",
        "      os.makedirs(path)\n",
        "    if pretrained and (pretrained_path is not None):\n",
        "      model.load_state_dict(torch.load(pretrained_path))\n",
        "    model.to(device)\n",
        "    for epoch in tqdm(range(1, num_epochs + 1)):\n",
        "        total_loss = 0\n",
        "        start_time = time.perf_counter()\n",
        "        model.train()\n",
        "        for inputs, labels in tqdm(train_loader, leave=False):\n",
        "          inputs, labels = inputs.to(device), labels.to(device)\n",
        "          optimizer.zero_grad()\n",
        "          output = model(inputs)\n",
        "          loss = criterion(output, labels)\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "          total_loss += loss\n",
        "        if path:\n",
        "          torch.save(model.state_dict(), f'{path}/model_ep_{epoch:02d}.pth')\n",
        "        end_time = time.perf_counter()\n",
        "        duration = end_time - start_time\n",
        "\n",
        "        train_acc = compute_accuracy(model, val_loader)\n",
        "\n",
        "\n",
        "\n",
        "        current_lr = optimizer.param_groups[0]['lr']\n",
        "\n",
        "        if scheduler and current_lr > 5e-5:\n",
        "            scheduler.step()\n",
        "\n",
        "        print(f'epoch {epoch:2}',\n",
        "              f'loss: {total_loss:.3f}',\n",
        "              f'time: {duration:.3f}',\n",
        "              f'val acc: {train_acc:.4f}',\n",
        "              sep='\\t')"
      ],
      "metadata": {
        "id": "hJFDhRBSa78P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category = {}\n",
        "cat_to_name = {\"21\": \"fire lily\", \"3\": \"canterbury bells\", \"45\": \"bolero deep blue\", \"1\": \"pink primrose\", \"34\": \"mexican aster\", \"27\": \"prince of wales feathers\", \"7\": \"moon orchid\", \"16\": \"globe-flower\", \"25\": \"grape hyacinth\", \"26\": \"corn poppy\", \"79\": \"toad lily\", \"39\": \"siam tulip\", \"24\": \"red ginger\", \"67\": \"spring crocus\", \"35\": \"alpine sea holly\", \"32\": \"garden phlox\", \"10\": \"globe thistle\", \"6\": \"tiger lily\", \"93\": \"ball moss\", \"33\": \"love in the mist\", \"9\": \"monkshood\", \"102\": \"blackberry lily\", \"14\": \"spear thistle\", \"19\": \"balloon flower\", \"100\": \"blanket flower\", \"13\": \"king protea\", \"49\": \"oxeye daisy\", \"15\": \"yellow iris\", \"61\": \"cautleya spicata\", \"31\": \"carnation\", \"64\": \"silverbush\", \"68\": \"bearded iris\", \"63\": \"black-eyed susan\", \"69\": \"windflower\", \"62\": \"japanese anemone\", \"20\": \"giant white arum lily\", \"38\": \"great masterwort\", \"4\": \"sweet pea\", \"86\": \"tree mallow\", \"101\": \"trumpet creeper\", \"42\": \"daffodil\", \"22\": \"pincushion flower\", \"2\": \"hard-leaved pocket orchid\", \"54\": \"sunflower\", \"66\": \"osteospermum\", \"70\": \"tree poppy\", \"85\": \"desert-rose\", \"99\": \"bromelia\", \"87\": \"magnolia\", \"5\": \"english marigold\", \"92\": \"bee balm\", \"28\": \"stemless gentian\", \"97\": \"mallow\", \"57\": \"gaura\", \"40\": \"lenten rose\", \"47\": \"marigold\", \"59\": \"orange dahlia\", \"48\": \"buttercup\", \"55\": \"pelargonium\", \"36\": \"ruby-lipped cattleya\", \"91\": \"hippeastrum\", \"29\": \"artichoke\", \"71\": \"gazania\", \"90\": \"canna lily\", \"18\": \"peruvian lily\", \"98\": \"mexican petunia\", \"8\": \"bird of paradise\", \"30\": \"sweet william\", \"17\": \"purple coneflower\", \"52\": \"wild pansy\", \"84\": \"columbine\", \"12\": \"colt's foot\", \"11\": \"snapdragon\", \"96\": \"camellia\", \"23\": \"fritillary\", \"50\": \"common dandelion\", \"44\": \"poinsettia\", \"53\": \"primula\", \"72\": \"azalea\", \"65\": \"californian poppy\", \"80\": \"anthurium\", \"76\": \"morning glory\", \"37\": \"cape flower\", \"56\": \"bishop of llandaff\", \"60\": \"pink-yellow dahlia\", \"82\": \"clematis\", \"58\": \"geranium\", \"75\": \"thorn apple\", \"41\": \"barbeton daisy\", \"95\": \"bougainvillea\", \"43\": \"sword lily\", \"83\": \"hibiscus\", \"78\": \"lotus lotus\", \"88\": \"cyclamen\", \"94\": \"foxglove\", \"81\": \"frangipani\", \"74\": \"rose\", \"89\": \"watercress\", \"73\": \"water lily\", \"46\": \"wallflower\", \"77\": \"passion flower\", \"51\": \"petunia\"}\n",
        "for key in cat_to_name:\n",
        "  k = int(key) - 1\n",
        "  category[f\"{k}\"] = cat_to_name[f'{key}']"
      ],
      "metadata": {
        "id": "1rzFAsjMl-3A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 30\n",
        "PATH = 'DINO_ViT_test.pth'\n",
        "MODEL_PATH = \"Finetuned_DINO_Vit.pth\"\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "vit = ViT_Dino(102)\n",
        "optimizer = torch.optim.Adam(vit.parameters(), lr=1e-4)\n",
        "\n",
        "finetune(vit, dataloaders['training'], dataloaders['validation'], 25, criterion, optimizer, MODEL_PATH, False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324,
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      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "beginning to train model\n"
          ]
        },
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              "  0%|          | 0/25 [00:00<?, ?it/s]"
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          "text": [
            "epoch  1\tloss: 45.328\ttime: 8.929\tval acc: 0.8449\n"
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            "epoch  2\tloss: 4.283\ttime: 8.844\tval acc: 0.9352\n"
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            "epoch  3\tloss: 0.869\ttime: 9.159\tval acc: 0.9647\n"
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            "epoch  4\tloss: 0.276\ttime: 8.222\tval acc: 0.9657\n"
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          "text": [
            "epoch  5\tloss: 0.113\ttime: 9.179\tval acc: 0.9725\n"
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            "epoch  6\tloss: 0.075\ttime: 8.601\tval acc: 0.9725\n"
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              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "83167285e5ed40949e392360f31608db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch  7\tloss: 0.092\ttime: 9.242\tval acc: 0.9755\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8a15691b7404c9095a1ff0f59cd2e8f"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee1a93bab17c4c45abc8a2cdc576c014"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch  8\tloss: 0.177\ttime: 9.218\tval acc: 0.9637\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d9c5b709a974280a4f14cad4d8a4308"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04adff5c20264e77a65e20c803e0ee4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch  9\tloss: 0.098\ttime: 8.310\tval acc: 0.9578\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a214f3bd6afc4f4485accebace5ef3b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef94163179bb4b639a18cbaea3152431"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 10\tloss: 0.086\ttime: 9.242\tval acc: 0.9706\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9184d52ad85d4bf293815269f53d9f9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d57900f8e54494e86f1a3f55ec228b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 11\tloss: 0.053\ttime: 8.454\tval acc: 0.9755\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4570ef82e7e14ce6be7bccfb6284c3fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee02fe3e96b64042a31a4d7a0ad81673"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 12\tloss: 0.045\ttime: 9.231\tval acc: 0.9764\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "07be9225034b4c8386603213b87548a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8aa9814c1ba34a82b5d6f6549e72cfad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 13\tloss: 0.037\ttime: 9.160\tval acc: 0.9715\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1d84f13887a47edbfbb81b14419070b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7daf93dddb1743b6add248695030cdea"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correct = 0\n",
        "count = 0\n",
        "for inputs, labels in dataloaders['testing']:\n",
        "  vit.cuda()\n",
        "  inputs, labels = inputs.cuda(), labels.cuda()\n",
        "  output = vit(inputs)\n",
        "  _, pred = torch.max(output, 1)\n",
        "  predicted = pred.item()\n",
        "  ground_truth = labels.item()\n",
        "  print(ground_truth, predicted)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3TRG_fuwPEko",
        "outputId": "0ae12bcc-addf-48f9-8be4-049ac29553dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "80 80\n",
            "58 58\n",
            "0 0\n",
            "45 45\n",
            "100 100\n",
            "89 89\n",
            "42 42\n",
            "50 50\n",
            "6 6\n",
            "23 23\n",
            "76 76\n",
            "36 36\n",
            "73 73\n",
            "72 72\n",
            "20 20\n",
            "36 36\n",
            "51 51\n",
            "74 74\n",
            "74 74\n",
            "40 58\n",
            "86 86\n",
            "35 35\n",
            "93 93\n",
            "31 31\n",
            "99 99\n",
            "54 54\n",
            "77 77\n",
            "50 50\n",
            "27 27\n",
            "87 87\n",
            "22 22\n",
            "80 80\n",
            "44 44\n",
            "72 72\n",
            "68 68\n",
            "50 50\n",
            "97 97\n",
            "22 22\n",
            "29 29\n",
            "41 41\n",
            "79 79\n",
            "87 87\n",
            "37 37\n",
            "93 93\n",
            "76 76\n",
            "76 76\n",
            "57 57\n",
            "77 77\n",
            "75 75\n",
            "45 45\n",
            "46 46\n",
            "94 94\n",
            "58 58\n",
            "67 67\n",
            "99 99\n",
            "97 89\n",
            "72 72\n",
            "59 59\n",
            "72 72\n",
            "72 72\n",
            "46 46\n",
            "63 63\n",
            "71 71\n",
            "19 19\n",
            "81 81\n",
            "13 13\n",
            "67 67\n",
            "74 74\n",
            "90 90\n",
            "84 84\n",
            "82 82\n",
            "50 50\n",
            "70 70\n",
            "72 77\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-54-f8b0190bbef9>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcorrect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'testing'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m   \u001b[0mvit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    628\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    631\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    672\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 674\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    675\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-45-f1ef7579611d>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mimg_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mimage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0my_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1064\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1066\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1067\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, index, col, takeable)\u001b[0m\n\u001b[1;32m   3912\u001b[0m         \"\"\"\n\u001b[1;32m   3913\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3914\u001b[0;31m             \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3915\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_ixs\u001b[0;34m(self, i, axis)\u001b[0m\n\u001b[1;32m   3729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3730\u001b[0m             \u001b[0mcol_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3731\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_col_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol_mgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3733\u001b[0m             \u001b[0;31m# this is a cached value, mark it so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_box_col_values\u001b[0;34m(self, values, loc)\u001b[0m\n\u001b[1;32m   4264\u001b[0m         \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4265\u001b[0m         \u001b[0;31m# We get index=self.index bc values is a SingleDataManager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4266\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4268\u001b[0m     \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PATH = \"\" #path to a dowloaded .jpg test image\n",
        "image = Image.open('/content/drive/MyDrive/COSMOSBIPINNATUSSensationCosmo_1.jpg').resize((300,300),resample=0)\n",
        "plt.imshow(image)\n",
        "plt.show()\n",
        "img_trans = transforms.Compose([\n",
        "        transforms.Resize(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "image = img_trans(image)\n",
        "image = torch.reshape(image, (1, 3, 224, 224))\n",
        "plt.imshow(torch.reshape(image, (224, 224, 3)))\n",
        "plt.show()\n",
        "vit.eval()\n",
        "vit.cuda()\n",
        "image = image.cuda()\n",
        "output = vit(image)\n",
        "_, pred = torch.max(output, 1)\n",
        "print(pred)\n",
        "print(\"MODEL PREDICTION: \", category[f\"{pred.item()}\"])"
      ],
      "metadata": {
        "id": "_VhMg5LMJZz_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}