vit_kdad.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Coding Project: Reproducing Salehi et al. with Transformers instead of VGG"
      ],
      "metadata": {
        "id": "A2JKxkT8So46"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Installing packages"
      ],
      "metadata": {
        "id": "sPiybuAIS6l7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kgYzMJfILnP",
        "outputId": "34c7d22a-f16e-4ba7-9c54-34a8cafe186a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Knowledge_Distillation_AD'...\n",
            "remote: Enumerating objects: 160, done.\u001b[K\n",
            "remote: Counting objects: 100% (130/130), done.\u001b[K\n",
            "remote: Compressing objects: 100% (77/77), done.\u001b[K\n",
            "remote: Total 160 (delta 65), reused 95 (delta 42), pack-reused 30\u001b[K\n",
            "Receiving objects: 100% (160/160), 20.21 MiB | 15.34 MiB/s, done.\n",
            "Resolving deltas: 100% (68/68), done.\n",
            "/content/Knowledge_Distillation_AD\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/LucStrater/Knowledge_Distillation_AD.git\n",
        "%cd /content/Knowledge_Distillation_AD"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# standard\n",
        "import os\n",
        "import numpy as np\n",
        "import random\n",
        "import math\n",
        "import json\n",
        "from functools import partial\n",
        "from PIL import Image\n",
        "import requests\n",
        "\n",
        "# plotting\n",
        "import matplotlib.pyplot as plt\n",
        "plt.set_cmap('cividis')\n",
        "%matplotlib inline\n",
        "from IPython.display import set_matplotlib_formats\n",
        "set_matplotlib_formats('svg', 'pdf') # For export\n",
        "from matplotlib.colors import to_rgb\n",
        "import matplotlib\n",
        "matplotlib.rcParams['lines.linewidth'] = 2.0\n",
        "import seaborn as sns\n",
        "sns.reset_orig()\n",
        "\n",
        "# tqdm for loading bars\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "# eval\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# torch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as data\n",
        "import torch.optim as optim\n",
        "\n",
        "# Torchvision\n",
        "import torchvision\n",
        "from torchvision.datasets import CIFAR10\n",
        "from torchvision import transforms\n",
        "\n",
        "# PyTorch Lightning\n",
        "try:\n",
        "    import pytorch_lightning as pl\n",
        "except ModuleNotFoundError:\n",
        "    !pip install --quiet pytorch-lightning>=1.4\n",
        "    import pytorch_lightning as pl\n",
        "from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "\n",
        "# Huggingface transformers\n",
        "try:\n",
        "    from transformers import ViTImageProcessor, ViTForImageClassification\n",
        "except ModuleNotFoundError:\n",
        "    !pip install --quiet transformers\n",
        "    from transformers import ViTImageProcessor, ViTForImageClassification\n",
        "\n",
        "# wandb\n",
        "try:\n",
        "    import wandb\n",
        "except ModuleNotFoundError:\n",
        "    !pip install --quiet wandb\n",
        "    import wandb\n",
        "\n",
        "# paths\n",
        "cwd = os.getcwd()\n",
        "DATASET_PATH = f\"{cwd}/data\"\n",
        "os.makedirs(DATASET_PATH, exist_ok=True)\n",
        "CHECKPOINT_PATH = f\"{cwd}/outputs/vit_checkpoints\"\n",
        "os.makedirs(CHECKPOINT_PATH, exist_ok=True)\n",
        "\n",
        "# seeding\n",
        "pl.seed_everything(42)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False\n",
        "\n",
        "# device\n",
        "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "print(\"Device:\", device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "gZGQ6dprWgfR",
        "outputId": "85c53bbf-011f-4576-e9fe-83fb9d867efe"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-bd86a10fefa7>:16: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
            "  set_matplotlib_formats('svg', 'pdf') # For export\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.6/190.6 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m243.9/243.9 kB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:lightning_fabric.utilities.seed:Seed set to 42\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load data"
      ],
      "metadata": {
        "id": "g76XNd7ETesA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wrote my own dataloader as the original dataloader did not include a seperate validation and test set. Also I wanted to add augmentations as I read online that they are necessary to make ViTs work. I found a pretrained ViT on huggingface that was already fine tuned on CIFAR10 which I want to use and therefor I need to rescale CIFAR10 to 224x224. Lastly, I wanted to understand the data a bit better as before this data loading had been quite vague to me."
      ],
      "metadata": {
        "id": "_cQghP5WowSu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_cifar10_data_loaders(normal_class, batch_size, path, num_workers, seed):\n",
        "    test_transform = transforms.Compose([transforms.Resize((256, 256), Image.LANCZOS),\n",
        "                                         transforms.CenterCrop(224),\n",
        "                                         transforms.ToTensor(),\n",
        "                                         transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n",
        "                                       ])\n",
        "    train_transform = transforms.Compose([transforms.Resize((256, 256), Image.LANCZOS),\n",
        "                                         transforms.RandomCrop(224),\n",
        "                                         transforms.RandomHorizontalFlip(),\n",
        "                                         transforms.ToTensor(),\n",
        "                                         transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n",
        "                                       ])\n",
        "\n",
        "    # load data\n",
        "    train_dataset = CIFAR10(root=path, train=True, transform=train_transform, download=True)\n",
        "    val_dataset = CIFAR10(root=path, train=True, transform=test_transform, download=True)\n",
        "\n",
        "    # split data\n",
        "    pl.seed_everything(seed)\n",
        "    train_indices, val_indices = torch.utils.data.random_split(train_dataset, [45000, 5000], generator=torch.Generator().manual_seed(seed))\n",
        "\n",
        "    # filter the training dataset\n",
        "    train_mask = np.array(train_dataset.targets) == normal_class\n",
        "    filtered_train_indices = [idx for idx in train_indices.indices if train_mask[idx]]\n",
        "\n",
        "    # create subsets\n",
        "    train_set = torch.utils.data.Subset(train_dataset, filtered_train_indices)\n",
        "    val_set = torch.utils.data.Subset(val_dataset, val_indices.indices)\n",
        "\n",
        "    # test set\n",
        "    test_set = CIFAR10(root=path, train=False, transform=test_transform, download=True)\n",
        "\n",
        "    # data loaders\n",
        "    train_loader = data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True, num_workers=num_workers)\n",
        "    val_loader = data.DataLoader(val_set, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers)\n",
        "    test_loader = data.DataLoader(test_set, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers)\n",
        "\n",
        "    return train_loader, val_loader, test_loader"
      ],
      "metadata": {
        "id": "SgT2yb1gTjxt"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader, val_loader, test_loader = prepare_cifar10_data_loaders(normal_class=3, batch_size=4, path=DATASET_PATH, num_workers=2, seed=42)\n",
        "\n",
        "dataiter = iter(val_loader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "L-IFRYVN9cHw",
        "outputId": "87510303-9feb-48f8-b1f5-146ff33c27d5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /content/Knowledge_Distillation_AD/data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:08<00:00, 20693713.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/Knowledge_Distillation_AD/data/cifar-10-python.tar.gz to /content/Knowledge_Distillation_AD/data\n",
            "Files already downloaded and verified\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:lightning_fabric.utilities.seed:Seed set to 42\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([7, 9, 1, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Understanding Huggingface pretrained ViTs"
      ],
      "metadata": {
        "id": "L5iSFx-LTPmE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This chapter is just to see how to use Hugginface pretrained ViT works."
      ],
      "metadata": {
        "id": "cCA0RDxXSKt3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "-nZf7oGViQUi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "referenced_widgets": [
            "19c1897430804487b4d2ea25d2896fc3",
            "a013f2afca28423a859d5a7743e1a535",
            "4f4c95a5905043e2b7b7378f228a816e",
            "ccc25197316444a5a67e566bccbaa772",
            "fc79b1db5e1c4900b0626d5883a26df6",
            "e25904a9573548b4af62e4806acf83a6",
            "8471085527bb4bbfada2482fedc681e4",
            "3e02d3f59386493b872727aaf085aeac",
            "31eae3faa5664391a741d7b08f05e56e",
            "b702cdef38dc413e9df64b7d977342aa",
            "5ef223c110e246fbb64d9559e5f6945c",
            "f11a3011e15648aea80b6db239b7faaa",
            "3192247f4e7843da809207cd58354a77",
            "bcaa079fb0524873a611b5c58739a5ae",
            "7a4dde5c69774452900cf6fc08da3909",
            "80db2fc6754e4fb4a6e109e5ac3ab3d2",
            "3a5aa6760e2c40ac816119d9662dff1e",
            "22a2407b51bf4a9389ad5e60db7fdd0f",
            "177a5ef40ca94d34af9b88c295bea260",
            "9a1f194c161e49aea657a4aa16d04b3f",
            "2c49c9b0b3e54fedaec573ca19225f64",
            "6678ab8237294d30b2e987b991b1d56d",
            "f27a8c2a3fc34884afd97505f2242917",
            "1d28ec0ddfcf4395bada161c59a84eb4",
            "30f74eee216e4fd99e791aec4d483574",
            "9ddc4b2d072c420796d15257060943fc",
            "9880911199ff45838dd88d4f92db2d54",
            "37f126b110bf4dd2b83c59e5ef91910c",
            "e05266c78ce6447f84127dd5b6c65bfd",
            "1f46d33bd77148f3aaced490b88c5ea2",
            "0ee219ddd811487cb17d273a7754a525",
            "05c1e83a3b04439893249c3b67ba3129",
            "e72f031b1cbb45c5a37b316098353a5f"
          ]
        },
        "outputId": "8aca57d3-7760-4608-d4ec-f789e1e71f09"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/918 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19c1897430804487b4d2ea25d2896fc3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/343M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f11a3011e15648aea80b6db239b7faaa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f27a8c2a3fc34884afd97505f2242917"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['horse', 'truck', 'automobile', 'cat']\n"
          ]
        }
      ],
      "source": [
        "vit_pretrained = ViTForImageClassification.from_pretrained('nateraw/vit-base-patch16-224-cifar10', output_hidden_states=True)\n",
        "processor_pretrained = ViTImageProcessor.from_pretrained('nateraw/vit-base-patch16-224-cifar10')\n",
        "\n",
        "# preprocess\n",
        "inputs = processor_pretrained(images=images, do_resize=False, do_rescale=False, do_normalize=False, return_tensors=\"pt\")\n",
        "\n",
        "# inference\n",
        "with torch.no_grad():\n",
        "    outputs = vit_pretrained(**inputs)\n",
        "\n",
        "# predicted class\n",
        "preds = outputs.logits.argmax(dim=1)\n",
        "\n",
        "classes = [\n",
        "    'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'\n",
        "]\n",
        "predicted_class = []\n",
        "for i in range(preds.shape[0]):\n",
        "  predicted_class.append(classes[preds[i].item()])\n",
        "\n",
        "print(predicted_class)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "yUWpOXuZiQUj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ce1d1fc-4c4b-425e-c9c4-601ea541ef4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([4, 197, 768])\n"
          ]
        }
      ],
      "source": [
        "print(outputs.hidden_states[12].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "PW7Pb4NsiQUj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80d0f123-76ff-4993-94da-faa218c4ba70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ViTForImageClassification(\n",
              "  (vit): ViTModel(\n",
              "    (embeddings): ViTEmbeddings(\n",
              "      (patch_embeddings): ViTPatchEmbeddings(\n",
              "        (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
              "      )\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): ViTEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x ViTLayer(\n",
              "          (attention): ViTAttention(\n",
              "            (attention): ViTSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): ViTSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ViTIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ViTOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "  )\n",
              "  (classifier): Linear(in_features=768, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "vit_pretrained"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Helper functions"
      ],
      "metadata": {
        "id": "4Ha-vCEnM_nQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For training the ViT model I need a number of helper functions, which I (mostly) did not make myself. Here is a list:\n",
        "\n",
        "* img_to_patch() and AttentionBlock() have been taken from a [tutorial](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial15/Vision_Transformer.html) by Philip Lippe. Aswell as VisionTransformer(), although that one I had to modify slightly to also output the hidden states.\n",
        "* MseDirectionLoss() is taken from the original code-base, and slightly modified to make the dimensions match."
      ],
      "metadata": {
        "id": "az2pNJX_OWas"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def img_to_patch(x, patch_size, flatten_channels=True):\n",
        "    \"\"\"\n",
        "    Inputs:\n",
        "        x - torch.Tensor representing the image of shape [B, C, H, W]\n",
        "        patch_size - Number of pixels per dimension of the patches (integer)\n",
        "        flatten_channels - If True, the patches will be returned in a flattened format\n",
        "                           as a feature vector instead of a image grid.\n",
        "    \"\"\"\n",
        "    B, C, H, W = x.shape\n",
        "    x = x.reshape(B, C, H//patch_size, patch_size, W//patch_size, patch_size)\n",
        "    x = x.permute(0, 2, 4, 1, 3, 5) # [B, H', W', C, p_H, p_W]\n",
        "    x = x.flatten(1,2)              # [B, H'*W', C, p_H, p_W]\n",
        "    if flatten_channels:\n",
        "        x = x.flatten(2,4)          # [B, H'*W', C*p_H*p_W]\n",
        "    return x"
      ],
      "metadata": {
        "id": "u6dpySHgQXPG"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AttentionBlock(nn.Module):\n",
        "\n",
        "    def __init__(self, embed_dim, hidden_dim, num_heads, dropout=0.0):\n",
        "        \"\"\"\n",
        "        Inputs:\n",
        "            embed_dim - Dimensionality of input and attention feature vectors\n",
        "            hidden_dim - Dimensionality of hidden layer in feed-forward network\n",
        "                         (usually 2-4x larger than embed_dim)\n",
        "            num_heads - Number of heads to use in the Multi-Head Attention block\n",
        "            dropout - Amount of dropout to apply in the feed-forward network\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "\n",
        "        self.layer_norm_1 = nn.LayerNorm(embed_dim)\n",
        "        self.attn = nn.MultiheadAttention(embed_dim, num_heads,\n",
        "                                          dropout=dropout)\n",
        "        self.layer_norm_2 = nn.LayerNorm(embed_dim)\n",
        "        self.linear = nn.Sequential(\n",
        "            nn.Linear(embed_dim, hidden_dim),\n",
        "            nn.GELU(),\n",
        "            nn.Dropout(dropout),\n",
        "            nn.Linear(hidden_dim, embed_dim),\n",
        "            nn.Dropout(dropout)\n",
        "        )\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        inp_x = self.layer_norm_1(x)\n",
        "        x = x + self.attn(inp_x, inp_x, inp_x)[0]\n",
        "        x = x + self.linear(self.layer_norm_2(x))\n",
        "        return x"
      ],
      "metadata": {
        "id": "_aV0EF2IQbiQ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class VisionTransformer(nn.Module):\n",
        "\n",
        "    def __init__(self, embed_dim, hidden_dim, num_channels, num_heads, num_layers, num_classes, patch_size, num_patches, dropout=0.0):\n",
        "        \"\"\"\n",
        "        Inputs:\n",
        "            embed_dim - Dimensionality of the input feature vectors to the Transformer\n",
        "            hidden_dim - Dimensionality of the hidden layer in the feed-forward networks\n",
        "                         within the Transformer\n",
        "            num_channels - Number of channels of the input (3 for RGB)\n",
        "            num_heads - Number of heads to use in the Multi-Head Attention block\n",
        "            num_layers - Number of layers to use in the Transformer\n",
        "            num_classes - Number of classes to predict\n",
        "            patch_size - Number of pixels that the patches have per dimension\n",
        "            num_patches - Maximum number of patches an image can have\n",
        "            dropout - Amount of dropout to apply in the feed-forward network and\n",
        "                      on the input encoding\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "\n",
        "        self.patch_size = patch_size\n",
        "\n",
        "        # Layers/Networks\n",
        "        self.input_layer = nn.Linear(num_channels*(patch_size**2), embed_dim)\n",
        "        self.transformer = nn.Sequential(*[AttentionBlock(embed_dim, hidden_dim, num_heads, dropout=dropout) for _ in range(num_layers)])\n",
        "        self.mlp_head = nn.Sequential(\n",
        "            nn.LayerNorm(embed_dim),\n",
        "            nn.Linear(embed_dim, num_classes)\n",
        "        )\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "        # Parameters/Embeddings\n",
        "        self.cls_token = nn.Parameter(torch.randn(1,1,embed_dim))\n",
        "        self.pos_embedding = nn.Parameter(torch.randn(1,1+num_patches,embed_dim))\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Preprocess input\n",
        "        x = img_to_patch(x, self.patch_size)\n",
        "        B, T, _ = x.shape\n",
        "        x = self.input_layer(x)\n",
        "\n",
        "        # Add CLS token and positional encoding\n",
        "        cls_token = self.cls_token.repeat(B, 1, 1)\n",
        "        x = torch.cat([cls_token, x], dim=1)\n",
        "        x = x + self.pos_embedding[:,:T+1]\n",
        "\n",
        "        # Apply Transforrmer\n",
        "        x = self.dropout(x)\n",
        "        x = x.transpose(0, 1)\n",
        "\n",
        "        # output hidden states\n",
        "        hidden_states = []\n",
        "        for block in self.transformer:\n",
        "            x = block(x)\n",
        "            hidden_states.append(x.transpose(0, 1))\n",
        "\n",
        "        # Perform classification prediction\n",
        "        cls = x[0]\n",
        "        out = self.mlp_head(cls)\n",
        "        return out, hidden_states"
      ],
      "metadata": {
        "id": "BlUZab_ZQg0e"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MseDirectionLoss(nn.Module):\n",
        "    def __init__(self, lamda):\n",
        "        super(MseDirectionLoss, self).__init__()\n",
        "        self.lamda = lamda\n",
        "        self.criterion = nn.MSELoss()\n",
        "        self.similarity_loss = torch.nn.CosineSimilarity()\n",
        "\n",
        "    def forward(self, output_pred, output_real):\n",
        "        y_pred_0, y_pred_1, y_pred_2, y_pred_3 = output_pred[0], output_pred[1], output_pred[2], output_pred[3]\n",
        "\n",
        "\n",
        "        y_0, y_1, y_2, y_3 = output_real[3], output_real[6], output_real[9], output_real[12]\n",
        "\n",
        "        # different terms of loss\n",
        "        abs_loss_0 = self.criterion(y_pred_0, y_0)\n",
        "        loss_0 = torch.mean(1 - self.similarity_loss(y_pred_0.view(y_pred_0.shape[0], -1), y_0.view(y_0.shape[0], -1)))\n",
        "        abs_loss_1 = self.criterion(y_pred_1, y_1)\n",
        "        loss_1 = torch.mean(1 - self.similarity_loss(y_pred_1.view(y_pred_1.shape[0], -1), y_1.view(y_1.shape[0], -1)))\n",
        "        abs_loss_2 = self.criterion(y_pred_2, y_2)\n",
        "        loss_2 = torch.mean(1 - self.similarity_loss(y_pred_2.view(y_pred_2.shape[0], -1), y_2.view(y_2.shape[0], -1)))\n",
        "        abs_loss_3 = self.criterion(y_pred_3, y_3)\n",
        "        loss_3 = torch.mean(1 - self.similarity_loss(y_pred_3.view(y_pred_3.shape[0], -1), y_3.view(y_3.shape[0], -1)))\n",
        "\n",
        "        total_loss = loss_0 + loss_1 + loss_2 + loss_3 + self.lamda * (\n",
        "                abs_loss_0 + abs_loss_1 + abs_loss_2 + abs_loss_3)\n",
        "\n",
        "        return total_loss"
      ],
      "metadata": {
        "id": "X_GCHDDNk994"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ViT model"
      ],
      "metadata": {
        "id": "d9UqHK66QlCp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is were the actual Knowledge Distillation for Anomaly Detection comes in. I decided to go for this pytorch lightning implementation so I needed to incorporate the detection_test() from the original code base here. The main idea is to use a ViT that is pretrained and fine-tuned on CIFAR10. Than to distill its capabilities into a smaller ViT that is trained here, the outputs of the hidden dimensions of multiple layers are forced to be similar to those of pretrained ViT."
      ],
      "metadata": {
        "id": "H5YssxvBR5Bc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class AD_ViT(pl.LightningModule):\n",
        "\n",
        "    def __init__(self, model_kwargs, lr, lamda, normal_class, hf_path, milestones, gamma):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "        self.model = VisionTransformer(**model_kwargs)\n",
        "\n",
        "        # get pretrained ViT\n",
        "        self.processor_pretrained = ViTImageProcessor.from_pretrained(hf_path)\n",
        "        self.vit_pretrained = ViTForImageClassification.from_pretrained(hf_path, output_hidden_states=True).to(self.device)\n",
        "\n",
        "        # freeze the pretrained model's parameters\n",
        "        for param in self.vit_pretrained.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "        # loss function\n",
        "        self.criterion = MseDirectionLoss(lamda)\n",
        "        self.similarity_loss = torch.nn.CosineSimilarity()\n",
        "\n",
        "        # init for evaluation\n",
        "        self.val_scores = []\n",
        "        self.val_labels = []\n",
        "        self.test_scores = []\n",
        "        self.test_labels = []\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)[0]\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = optim.AdamW(self.parameters(), lr=self.hparams.lr)\n",
        "        lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=self.hparams.milestones, gamma=self.hparams.gamma)\n",
        "        return [optimizer], [lr_scheduler]\n",
        "\n",
        "    def _step(self, batch, mode=\"train\"):\n",
        "        images, _ = batch\n",
        "        images = images.to(self.device)\n",
        "\n",
        "        # inference\n",
        "        output_pred = self.model(images)[1]\n",
        "\n",
        "        # inference with the pretrained model\n",
        "        inputs = self.processor_pretrained(images=images, do_resize=False, do_rescale=False, do_normalize=False, return_tensors=\"pt\")\n",
        "        inputs = {k: v.to(self.device) for k, v in inputs.items()}\n",
        "        output_real = self.vit_pretrained(**inputs).hidden_states\n",
        "\n",
        "        # loss\n",
        "        loss = self.criterion(output_pred, output_real)\n",
        "        self.log(f'{mode}_loss', loss)\n",
        "\n",
        "        return loss, output_pred, output_real\n",
        "\n",
        "    def _eval_step(self, batch, mode=\"val\"):\n",
        "        loss, output_pred, output_real = self._step(batch, mode=mode)\n",
        "\n",
        "        _, labels = batch\n",
        "        target_class = self.hparams.normal_class\n",
        "        labels = torch.where(labels == target_class, 1, 0)\n",
        "\n",
        "        y_pred_1, y_pred_2, y_pred_3 = output_pred[1], output_pred[2], output_pred[3]\n",
        "        y_1, y_2, y_3 = output_real[6], output_real[9], output_real[12]\n",
        "\n",
        "        abs_loss_1 = torch.mean((y_pred_1 - y_1) ** 2, dim=(1, 2))\n",
        "        loss_1 = 1 - self.similarity_loss(y_pred_1.view(y_pred_1.shape[0], -1), y_1.view(y_1.shape[0], -1))\n",
        "        abs_loss_2 = torch.mean((y_pred_2 - y_2) ** 2, dim=(1, 2))\n",
        "        loss_2 = 1 - self.similarity_loss(y_pred_2.view(y_pred_2.shape[0], -1), y_2.view(y_2.shape[0], -1))\n",
        "        abs_loss_3 = torch.mean((y_pred_3 - y_3) ** 2, dim=(1, 2))\n",
        "        loss_3 = 1 - self.similarity_loss(y_pred_3.view(y_pred_3.shape[0], -1), y_3.view(y_3.shape[0], -1))\n",
        "        scores = loss_1 + loss_2 + loss_3 + self.hparams.lamda * (abs_loss_1 + abs_loss_2 + abs_loss_3)\n",
        "\n",
        "        return scores, labels\n",
        "\n",
        "    def _eval_epoch_end(self, scores, labels, mode=\"val\"):\n",
        "        all_scores = torch.cat(scores, dim=0)\n",
        "        all_labels = torch.cat(labels, dim=0)\n",
        "\n",
        "        # numpy\n",
        "        scores_np = all_scores.view(-1).cpu().numpy()\n",
        "        labels_np = all_labels.view(-1).cpu().numpy()\n",
        "\n",
        "        # calculate ROC AUC\n",
        "        fpr, tpr, thresholds = roc_curve(labels_np, scores_np, pos_label=0)\n",
        "        roc_auc = auc(fpr, tpr)\n",
        "        roc_auc = round(roc_auc, 4)\n",
        "        self.log(f'{mode}_roc_auc', roc_auc, on_epoch=True, prog_bar=True)\n",
        "\n",
        "        return roc_auc\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        loss, _, _ = self._step(batch, mode=\"train\")\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        scores, labels = self._eval_step(batch, mode=\"val\")\n",
        "        self.val_scores.append(scores)\n",
        "        self.val_labels.append(labels)\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        scores, labels = self._eval_step(batch, mode=\"test\")\n",
        "        self.test_scores.append(scores)\n",
        "        self.test_labels.append(labels)\n",
        "\n",
        "    def on_validation_epoch_end(self):\n",
        "        val_result = self._eval_epoch_end(self.val_scores, self.val_labels, mode=\"val\")\n",
        "        self.val_scores = []\n",
        "        self.val_labels = []\n",
        "\n",
        "    def on_test_epoch_end(self):\n",
        "        test_result = self._eval_epoch_end(self.test_scores, self.test_labels, mode=\"test\")\n",
        "        self.test_scores = []\n",
        "        self.test_labels = []\n",
        "\n"
      ],
      "metadata": {
        "id": "tOzpxDvYR2ID"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training\n",
        "This is standard huggingface stuff."
      ],
      "metadata": {
        "id": "_7wY2v2gSIJw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(**kwargs):\n",
        "    normal_class = kwargs.get('normal_class')\n",
        "    seed = 42\n",
        "\n",
        "    # logging\n",
        "    wandb.init(project='AD_ViT_CIFAR10', entity='lucstrater', name=f'class_{normal_class}')\n",
        "    wandb_logger = WandbLogger()\n",
        "\n",
        "    trainer = pl.Trainer(log_every_n_steps=5,\n",
        "                         logger=wandb_logger,\n",
        "                         default_root_dir=os.path.join(CHECKPOINT_PATH, f'AD_ViT_CIFAR10_class_{normal_class}'),\n",
        "                         accelerator=\"gpu\" if str(device).startswith(\"cuda\") else \"cpu\",\n",
        "                         devices=1,\n",
        "                         max_epochs=10,\n",
        "                         callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_roc_auc\"),\n",
        "                                    LearningRateMonitor(\"epoch\")])\n",
        "\n",
        "    # get dataloaders\n",
        "    train_loader, val_loader, test_loader = prepare_cifar10_data_loaders(normal_class=normal_class, batch_size=128, path=DATASET_PATH, num_workers=2, seed=seed)\n",
        "\n",
        "    # train\n",
        "    pl.seed_everything(seed)\n",
        "    model = AD_ViT(**kwargs)\n",
        "    trainer.fit(model, train_loader, val_loader)\n",
        "    model = AD_ViT.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n",
        "\n",
        "    # test\n",
        "    val_result = trainer.test(model, val_loader, verbose=False)\n",
        "    test_result = trainer.test(model, test_loader, verbose=False)\n",
        "    result = {\"test\": test_result[0][\"test_roc_auc\"], \"val\": val_result[0][\"test_roc_auc\"]}\n",
        "\n",
        "    # logging\n",
        "    wandb.finish()\n",
        "\n",
        "    return model, result"
      ],
      "metadata": {
        "id": "9eSYHzo5SRVr"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wandb.login()\n",
        "\n",
        "model, results = train_model(model_kwargs={\n",
        "                                'embed_dim': 768,\n",
        "                                'hidden_dim': 512,\n",
        "                                'num_heads': 8,\n",
        "                                'num_layers': 4,\n",
        "                                'patch_size': 16,\n",
        "                                'num_channels': 3,\n",
        "                                'num_patches': 196,\n",
        "                                'num_classes': 10,\n",
        "                                'dropout': 0.2\n",
        "                            },\n",
        "                            lr=5e-5, lamda=0.01, normal_class=0, hf_path='nateraw/vit-base-patch16-224-cifar10', milestones=[5], gamma=0.2)\n",
        "print(\"ViT results\", results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇█</td></tr><tr><td>lr-AdamW</td><td>█████▁▁▁▁▁</td></tr><tr><td>test_loss</td><td>█▁</td></tr><tr><td>test_roc_auc</td><td>█▁</td></tr><tr><td>train_loss</td><td>██▆▅▅▅▄▄▄▃▄▄▃▃▃▃▂▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▂▁</td></tr><tr><td>trainer/global_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████</td></tr><tr><td>val_loss</td><td>▆█▆▃▂▂▂▁▂▁</td></tr><tr><td>val_roc_auc</td><td>▁▆▇▇██████</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>10</td></tr><tr><td>lr-AdamW</td><td>1e-05</td></tr><tr><td>test_loss</td><td>9.39199</td></tr><tr><td>test_roc_auc</td><td>0.9678</td></tr><tr><td>train_loss</td><td>6.69794</td></tr><tr><td>trainer/global_step</td><td>350</td></tr><tr><td>val_loss</td><td>9.37214</td></tr><tr><td>val_roc_auc</td><td>0.9685</td></tr></table><br/></div></div>"
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