Dementia_ViT.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rmezapi/dementia_vit/blob/main/Dementia_ViT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This project was intended to test the limits of the ViT on a tough dementia dataset. The data used can be found on HuggingFace at: https://huggingface.co/datasets/Falah/Alzheimer_MRI. The project follows closely the following tutorials:\n",
        "\n",
        "\n",
        "*   https://www.youtube.com/watch?v=r88L_yLJ4CE&ab_channel=code_your_own_AI\n",
        "*   https://www.youtube.com/watch?v=qU7wO02urYU&ab_channel=JamesBriggs  \n",
        "\n",
        "\n",
        "I modify the code presented in the video and tune all parameters to optimize performance using mostly the same libraries and tools. This is a practice project for myself as I return to coding/designing ML models after dedicating time to AI/ML theory (model architectures, transfer learning)"
      ],
      "metadata": {
        "id": "VEc3ZOLUPota"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94ISwNbfKAkc",
        "outputId": "a4d30659-3dce-4c67-9d18-11450c5dfd37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping pytorch-accelerated as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: transformers 4.26.0 does not provide the extra 'utils'\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "!pip install -q torch torchvision\n",
        "import torch\n",
        "import torchvision\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "!pip install -q transformers datasets\n",
        "!pip uninstall -q accelerate -y\n",
        "!pip uninstall -q pytorch-accelerated -y\n",
        "!pip install -q accelerate==0.21.0\n",
        "!pip install -q transformers==4.26.0\n",
        "!pip install -q transformers[torch]\n",
        "!pip install -q transformers[utils]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import ViTConfig, ViTModel\n",
        "\n",
        "# initalizing a ViT vit-patch-base16-224 configuration\n",
        "config = ViTConfig()\n",
        "# initialize a model w random weights\n",
        "model = ViTModel(config)\n",
        "# access the model config\n",
        "config = model.config"
      ],
      "metadata": {
        "id": "z2xSSAVvKfZW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OI0c2hdvNBwe",
        "outputId": "aadab663-c621-43c8-d5f2-073f072e7e35"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ViTConfig {\n",
              "  \"attention_probs_dropout_prob\": 0.0,\n",
              "  \"encoder_stride\": 16,\n",
              "  \"hidden_act\": \"gelu\",\n",
              "  \"hidden_dropout_prob\": 0.0,\n",
              "  \"hidden_size\": 768,\n",
              "  \"image_size\": 224,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 3072,\n",
              "  \"layer_norm_eps\": 1e-12,\n",
              "  \"model_type\": \"vit\",\n",
              "  \"num_attention_heads\": 12,\n",
              "  \"num_channels\": 3,\n",
              "  \"num_hidden_layers\": 12,\n",
              "  \"patch_size\": 16,\n",
              "  \"qkv_bias\": true,\n",
              "  \"transformers_version\": \"4.26.0\"\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLDBIyzeKTPk",
        "outputId": "6c9c600b-f641-479d-ed59-945627946b64"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "  (pooler): ViTPooler(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (activation): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import ViTImageProcessor\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"Falah/Alzheimer_MRI\")\n"
      ],
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            "23c1b67f338542fcb61246b3f14d40bb",
            "109b565b10f9437eb22be06a6c1c723a",
            "eb689a48851f422aba8efb4eb155dfbf",
            "49c8121a87bf46579d731a83f832841a",
            "4094be52f4904873b66f3707e17a4e59",
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            "1a8674acecc941f58cfcdb3aeff74b33",
            "b5c0b89ffdf14cb3a76b99b8d72e8a09",
            "892c12500def435884c92e55797c095f",
            "7cb3d617260241eda4e2e16cfdad61b6",
            "5948c93ee8af40ad981a3be629709621",
            "61cf3a81cd5345c7858db0df0244d92d",
            "1ad42c2642f34108a853e6016b2602c7",
            "c30ef88b0e8b4debbed02d1c36c06cce",
            "1af6f661e36348ef8373230d1beef237",
            "1d58ecf09a694b14ab5fe40c450ac9ff",
            "e63cc71ab49c4d049366009705e8e37e",
            "72ecb626020f4118bd5f77743a0b5d6d",
            "5edfaa686c724400b8ce54150e613954",
            "dd664147169b41b2881ac0b21d905f6d",
            "059ff722c2d2479e866f66effce7b138",
            "f922da73be174500afef08c836247c8e",
            "97e52126edf74918b5aba0868226522a",
            "d9d9ad2666004d6a830babc585e547b2",
            "96adee706dc34786911395e6b24bb697",
            "e87d8ebc83a14a4487670162c5557cff",
            "2d7c4a3993d74d6bb2a7094c1ec521d1",
            "40741812bca049d9a82197d0e1603bb2",
            "3da3c9acac8d4709af37a81c642d538d",
            "fc39dd68403041f499f0bb4206567d0c",
            "022420983eac48409c5991d76454cd35",
            "b501e3cc2a6e4f54bb67adbfa68ff215",
            "81162d705bf4434084878b8e63555bfd"
          ]
        },
        "id": "6JtqJNz0NRNy",
        "outputId": "5c741f0f-3d95-4df6-ce10-c42945638523"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.13k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64f554424d1c4efea26d5218dafbef22"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/22.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ab55c0b3fb2466b87de0d79ca0cb978"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/5.65M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49c8121a87bf46579d731a83f832841a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/5120 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61cf3a81cd5345c7858db0df0244d92d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1280 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97e52126edf74918b5aba0868226522a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# preview the 1st image\n",
        "image = dataset[\"train\"][0][\"image\"].resize((224, 224))\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "9_hx4yrjOmRA",
        "outputId": "502464b0-1a7a-4f4f-b59d-3909207459b7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# construct a ViT image processor\n",
        "features = ViTImageProcessor.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n",
        "# load pretrained model on imagenet-21k\n",
        "model = ViTModel.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "e928fd13a6d44221b81724ad58fa9924",
            "9e2a35a9c09f48559aa294facad3424e",
            "ec68a0dc59c243b88de4ea6a22fb4740",
            "53c68cdb363b45468bede9c7ad7f26b7",
            "1adbe552d03a4a1280c3e39e76451acc",
            "777e3df2395d4427b76b416b66ab739b",
            "49647669c517490b9692b9ba44617dcd",
            "ac2f868962924d7592680628295a3072",
            "0113d1c10a094d68868474971ebfb5da",
            "2d0df2ef3ce34cbe82030f35c6b3d3fa",
            "df497883721e499ca12ea9d774086f03",
            "998875f486304599b812a5331573aa7f",
            "d8b66e58abfe408c9047a9533fc183e4",
            "9871f0b6d31044dfa06b023dab697260",
            "1259ce6b190d4741ab12f43e53cb4447",
            "ae7df15835174304bec8f1e39dd51289",
            "c4473b591111422aac8bf2e0d62e26e0",
            "7049d92cca3143fda827599b235dd22e",
            "000c110bfab14bdfbaa8f2e52bec1e7a",
            "2d5d2c351c95469c87b6542c616e85ed",
            "a524340b511145b087d6bac0f05cbe24",
            "26b00cd6d6124dba8ede7603be931506",
            "c2a206d3ca254037b9acf7ec1410728b",
            "64a436690a684b749983f208e2e54dbe",
            "6a428951222b4526b30ea3a9cd119c57",
            "4a68dcaaabeb4c819698adefedd3c757",
            "184ebb7c25a848f8b6cfa0c8ba9743ec",
            "63bad86498b94881bb85df111d23f9fa",
            "369fa47525174b7e8ff04ed93064aa43",
            "f7a992223911466c92009a8e1c189e14",
            "bb813e9b59a346f393a2955017f3c974",
            "93c016d3eff44a209eb29b31f0c97980",
            "b26c065cd1784819b141de05ca1d3f03"
          ]
        },
        "id": "0J7SG9qKO0Qp",
        "outputId": "4d09a47d-d674-4b62-b9f8-1627e513ff84"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e928fd13a6d44221b81724ad58fa9924"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "998875f486304599b812a5331573aa7f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2a206d3ca254037b9acf7ec1410728b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features.rescale_factor = 1\n",
        "features.size = {'height':224, 'width':224}"
      ],
      "metadata": {
        "id": "_0S_QnHWPb26"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# add channels to image\n",
        "image = image.convert(mode=\"RGB\").resize((224, 224))\n",
        "\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "iosDVzpOQiT5",
        "outputId": "849636fa-ccd6-48e4-86e5-bc3db40c25a4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = features(image, return_tensors=\"pt\")\n",
        "\n",
        "with torch.no_grad():\n",
        "  outputs = model(**inputs)"
      ],
      "metadata": {
        "id": "5iPiZJ06QaRO"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "last_hidden_state = outputs.last_hidden_state\n",
        "last_hidden_state.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jJs3j-DSOhs",
        "outputId": "3cb9d229-fda2-4cf7-a789-347e870e36cb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 197, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7g-wnQCS1nz",
        "outputId": "53630e12-0777-4c30-fea6-16e2c68e5063"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ViTConfig {\n",
              "  \"_name_or_path\": \"google/vit-base-patch16-224-in21k\",\n",
              "  \"architectures\": [\n",
              "    \"ViTModel\"\n",
              "  ],\n",
              "  \"attention_probs_dropout_prob\": 0.0,\n",
              "  \"encoder_stride\": 16,\n",
              "  \"hidden_act\": \"gelu\",\n",
              "  \"hidden_dropout_prob\": 0.0,\n",
              "  \"hidden_size\": 768,\n",
              "  \"image_size\": 224,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 3072,\n",
              "  \"layer_norm_eps\": 1e-12,\n",
              "  \"model_type\": \"vit\",\n",
              "  \"num_attention_heads\": 12,\n",
              "  \"num_channels\": 3,\n",
              "  \"num_hidden_layers\": 12,\n",
              "  \"patch_size\": 16,\n",
              "  \"qkv_bias\": true,\n",
              "  \"transformers_version\": \"4.26.0\"\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGKxQSteVPJU",
        "outputId": "9e032afb-ed82-46cf-f5a9-6172730b9ece"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess(batch):\n",
        "  # Convert the images to RGB format (3 channels)\n",
        "  images = [image.convert(\"RGB\") for image in batch[\"image\"]]\n",
        "\n",
        "  # take a list of images and turn them into pixel values\n",
        "  inputs = features(images, return_tensors=\"pt\")\n",
        "  # Check if the 'image' key is present\n",
        "  if \"image\" in inputs:\n",
        "      inputs.pop(\"image\")\n",
        "\n",
        "  # include the labels\n",
        "  inputs[\"labels\"] = batch[\"label\"]\n",
        "  return inputs"
      ],
      "metadata": {
        "id": "PxxgB3fwVSJD"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_train = dataset[\"train\"].with_transform(preprocess)\n",
        "prepared_test = dataset[\"test\"].with_transform(preprocess)"
      ],
      "metadata": {
        "id": "2i1DhVKfVpgI"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# collate function\n",
        "def collate(batch):\n",
        "  return {\n",
        "      \"pixel_values\": torch.stack([x[\"pixel_values\"] for x in batch]),\n",
        "      \"labels\": torch.tensor([x[\"labels\"] for x in batch])\n",
        "  }"
      ],
      "metadata": {
        "id": "rbuAVAVUVypB"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")\n",
        "def metric_fn(eval_pred):\n",
        "  logits, labels = eval_pred\n",
        "  predictions = np.argmax(logits, axis=-1)\n",
        "  return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4d407da13e684d679d8c3214b02dfe32",
            "9d98be15e25c4c5b99699728aad83e97",
            "2f844734b0fa45729b15301f77d15022",
            "07b72901bde846aa8415179a1408f1d6",
            "8a89e187a2f1495e994eea4161136c83",
            "a1a157434ccb40a2b36a9e528ad1e2cc",
            "6b4b0bbb96f9418c9425dc1bee9ea243",
            "a636cd357e294419956e660594ce74ef",
            "213fc269c5e548a8b180fbd7f1af708f",
            "6e79d4bc02834efaba0f4937fb87e953",
            "79b1f453cfbc4226b3eba1585f84dacf"
          ]
        },
        "id": "pkRbDC-iWTRA",
        "outputId": "cad2c6d9-c3f6-4470-e90b-0e5d5940be79"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d407da13e684d679d8c3214b02dfe32"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./dementia\",\n",
        "    per_device_train_batch_size=16,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    num_train_epochs=5,\n",
        "    learning_rate=2e-5,\n",
        "    weight_decay=0.001,\n",
        "    push_to_hub=False,\n",
        "    report_to=\"none\",\n",
        "    load_best_model_at_end=True,\n",
        "    save_total_limit=3,\n",
        "    logging_steps=1,\n",
        "    save_steps=1,\n",
        "    remove_unused_columns=False\n",
        ")"
      ],
      "metadata": {
        "id": "j3PqJRLQWloD"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import ViTForImageClassification\n",
        "\n",
        "labels = dataset[\"train\"].features[\"label\"].names\n",
        "model = ViTForImageClassification.from_pretrained(\n",
        "    \"google/vit-base-patch16-224-in21k\",\n",
        "    num_labels=len(labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_Ywf5-LaG-X",
        "outputId": "e88db38a-49e8-4536-9890-1bd3eb5fcf9c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing ViTForImageClassification: ['pooler.dense.weight', 'pooler.dense.bias']\n",
            "- This IS expected if you are initializing ViTForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing ViTForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dYLkmzynr5P",
        "outputId": "78d231b1-bbed-4282-d492-acb5cdd1bd0d"
      },
      "execution_count": 20,
      "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=4, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=prepared_train,\n",
        "    eval_dataset=prepared_test,\n",
        "    tokenizer=features,\n",
        "    data_collator=collate,\n",
        "    compute_metrics=metric_fn\n",
        ")"
      ],
      "metadata": {
        "id": "35Xl6GWMnv6i"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train and store results\n",
        "train_results = trainer.train()\n",
        "# save tokenizer w the model\n",
        "trainer.save_model()\n",
        "# log and save metrics\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "# save the trainer state\n",
        "trainer.save_state()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aQT13_WBnv3a",
        "outputId": "3d1a1c11-837d-4642-f534-b9cb79d6aca2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 5120\n",
            "  Num Epochs = 5\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1600\n",
            "  Number of trainable parameters = 85801732\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1600' max='1600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1600/1600 05:24, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.817400</td>\n",
              "      <td>0.927409</td>\n",
              "      <td>0.560156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.644300</td>\n",
              "      <td>0.892673</td>\n",
              "      <td>0.572656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.716100</td>\n",
              "      <td>0.918184</td>\n",
              "      <td>0.576562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.621800</td>\n",
              "      <td>0.833306</td>\n",
              "      <td>0.608594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.720200</td>\n",
              "      <td>0.784546</td>\n",
              "      <td>0.659375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1280\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./dementia/checkpoint-320\n",
            "Configuration saved in ./dementia/checkpoint-320/config.json\n",
            "Model weights saved in ./dementia/checkpoint-320/pytorch_model.bin\n",
            "Image processor saved in ./dementia/checkpoint-320/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1280\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./dementia/checkpoint-640\n",
            "Configuration saved in ./dementia/checkpoint-640/config.json\n",
            "Model weights saved in ./dementia/checkpoint-640/pytorch_model.bin\n",
            "Image processor saved in ./dementia/checkpoint-640/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1280\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./dementia/checkpoint-960\n",
            "Configuration saved in ./dementia/checkpoint-960/config.json\n",
            "Model weights saved in ./dementia/checkpoint-960/pytorch_model.bin\n",
            "Image processor saved in ./dementia/checkpoint-960/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1280\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./dementia/checkpoint-1280\n",
            "Configuration saved in ./dementia/checkpoint-1280/config.json\n",
            "Model weights saved in ./dementia/checkpoint-1280/pytorch_model.bin\n",
            "Image processor saved in ./dementia/checkpoint-1280/preprocessor_config.json\n",
            "Deleting older checkpoint [dementia/checkpoint-320] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1280\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./dementia/checkpoint-1600\n",
            "Configuration saved in ./dementia/checkpoint-1600/config.json\n",
            "Model weights saved in ./dementia/checkpoint-1600/pytorch_model.bin\n",
            "Image processor saved in ./dementia/checkpoint-1600/preprocessor_config.json\n",
            "Deleting older checkpoint [dementia/checkpoint-640] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from ./dementia/checkpoint-1600 (score: 0.7845457792282104).\n",
            "Saving model checkpoint to ./dementia\n",
            "Configuration saved in ./dementia/config.json\n",
            "Model weights saved in ./dementia/pytorch_model.bin\n",
            "Image processor saved in ./dementia/preprocessor_config.json\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =          5.0\n",
            "  total_flos               = 1847586123GF\n",
            "  train_loss               =       0.8488\n",
            "  train_runtime            =   0:05:25.55\n",
            "  train_samples_per_second =       78.635\n",
            "  train_steps_per_second   =        4.915\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view eval metrics\n",
        "metrics = trainer.evaluate(prepared_test)\n",
        "trainer.log_metrics(\"eval\", metrics)\n",
        "trainer.save_metrics(\"eval\", metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "gA8AkQzinv0R",
        "outputId": "d5bb5505-6d27-427c-cebc-7e136e179b14"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1280\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='160' max='160' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [160/160 00:06]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** eval metrics *****\n",
            "  epoch                   =        5.0\n",
            "  eval_accuracy           =     0.6594\n",
            "  eval_loss               =     0.7845\n",
            "  eval_runtime            = 0:00:06.63\n",
            "  eval_samples_per_second =    192.794\n",
            "  eval_steps_per_second   =     24.099\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "from random import randint\n",
        "n_sample = randint(0, len(dataset[\"test\"]))\n",
        "image = dataset[\"test\"][n_sample][\"image\"].resize((224, 224))\n",
        "\n",
        "# 0: mild_demented, 1: moderate_demented, 2: non_demented, 3: very_mild_demented\n",
        "\n",
        "ground_truth = dataset[\"test\"][n_sample][\"label\"]\n",
        "\n",
        "print(f\"Ground Truth: {labels[ground_truth]} ({ground_truth})\")\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "d5fwuuC5nvw6",
        "outputId": "febbe9cc-bc9b-4d8d-f9e9-c34651223055"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ground Truth: Very_Mild_Demented (3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the model output\n",
        "image = image.convert(\"RGB\")\n",
        "inputs = features(image, return_tensors=\"pt\").to(device)\n",
        "with torch.no_grad():\n",
        "  outputs = model(**inputs).logits\n",
        "\n",
        "predicted_label = torch.argmax(outputs).item()\n",
        "print(f\"Ground Truth: {labels[ground_truth]} ({ground_truth})\")\n",
        "print(f\"Predicted Label: {labels[predicted_label]} ({predicted_label})\")\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "Y3ED2LN4_AA5",
        "outputId": "85ec2945-940b-40f5-e521-597875cee410"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ground Truth: Very_Mild_Demented (3)\n",
            "Predicted Label: Very_Mild_Demented (3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    }
  ]
}