AnoMed_PyTorch_ViT.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorboard datasets -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99wuhxexO5Gl",
        "outputId": "a18821af-a2d5-48cc-82ef-d20f041868cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/527.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m51.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "from torch.optim.lr_scheduler import ExponentialLR\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from torchvision import transforms\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from datasets import load_dataset\n",
        "from PIL import Image"
      ],
      "metadata": {
        "id": "ayrXkEmBQclT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(0)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "IyHTBzExQb5E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "data_dir = \"./data\"\n",
        "celeba = load_dataset(\"tpremoli/CelebA-attrs\", split=\"all\", cache_dir=data_dir)\n",
        "\n",
        "df = celeba.to_pandas()\n",
        "\n",
        "attr_names = df.columns[1:-1]\n",
        "image_col_name = df.columns[0]\n",
        "\n",
        "attributes = df[attr_names].values.astype(np.float32)\n",
        "\n",
        "inputs = df[image_col_name].values\n",
        "\n",
        "targets = (attributes + 1) / 2\n",
        "\n",
        "train_percentage = 0.8\n",
        "val_percentage = 0.1\n",
        "test_percentage = 1 - train_percentage - val_percentage\n",
        "\n",
        "training_amount = int(train_percentage * len(inputs))\n",
        "validation_amount = int(val_percentage * len(inputs))\n",
        "testing_amount = int(train_percentage * len(inputs))\n",
        "\n",
        "train_inputs = inputs[0:training_amount]\n",
        "val_inputs = inputs[training_amount:training_amount+validation_amount]\n",
        "test_inputs = inputs[training_amount+validation_amount:]\n",
        "\n",
        "\n",
        "train_targets = targets[0:training_amount]\n",
        "val_targets = targets[training_amount:training_amount+validation_amount]\n",
        "test_targets = targets[training_amount+validation_amount:]\n",
        "\n",
        "train_targets = torch.tensor(train_targets, dtype=torch.float32).to(device)\n",
        "val_targets = torch.tensor(val_targets, dtype=torch.float32).to(device)\n",
        "test_targets = torch.tensor(test_targets, dtype=torch.float32).to(device)"
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        "outputId": "f11b9a76-a33d-4483-f625-58bb8d8d850a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.74k [00:00<?, ?B/s]"
            ],
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        {
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          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "a25052a42df048e2bfa0f7015d069c7d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a8y8BVa-OgdQ"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "from io import BytesIO\n",
        "\n",
        "# Define custom transform\n",
        "custom_transform = transforms.Compose([\n",
        "    transforms.CenterCrop((178, 178)),\n",
        "    transforms.Resize((128, 128)),\n",
        "    transforms.ToTensor()\n",
        "])\n",
        "\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "class ViTDataset(Dataset):\n",
        "    def __init__(self, images, targets, feature_extractor):\n",
        "        self.images = images  # List of image paths or byte data dictionaries\n",
        "        self.targets = targets  # Corresponding targets\n",
        "        self.feature_extractor = feature_extractor\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        image_data = self.images[idx]\n",
        "\n",
        "        if isinstance(image_data, dict):  # If image data is stored as a dict (e.g., {'bytes': ...})\n",
        "            img_bytes = image_data['bytes']\n",
        "            image = Image.open(BytesIO(img_bytes))\n",
        "        else:  # Otherwise, assume it's a file path\n",
        "            image = Image.open(image_data)\n",
        "\n",
        "        # Apply feature extractor\n",
        "        encoded_input = self.feature_extractor(images=image, return_tensors=\"pt\")\n",
        "        pixel_values = encoded_input['pixel_values'].squeeze(0)  # Remove the batch dimension\n",
        "\n",
        "        target = self.targets[idx]\n",
        "        return pixel_values, target"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs=100):\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        train_loss = 0.0\n",
        "\n",
        "        for images, targets in train_loader:\n",
        "            images = images.to(device)\n",
        "            targets = targets.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(images).logits  # Access logits directly\n",
        "            loss = criterion(outputs, targets)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            train_loss += loss.item()\n",
        "\n",
        "        avg_train_loss = train_loss / len(train_loader)\n",
        "\n",
        "        model.eval()\n",
        "        eval_loss = 0.0\n",
        "        correct_predictions = 0\n",
        "        total_predictions = 0\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for images, targets in val_loader:\n",
        "                images = images.to(device)\n",
        "                targets = targets.to(device)\n",
        "\n",
        "                outputs = model(images).logits  # Access logits directly\n",
        "                loss = criterion(outputs, targets)\n",
        "                eval_loss += loss.item()\n",
        "\n",
        "                predictions = torch.sigmoid(outputs)  # Apply sigmoid to get probabilities\n",
        "                predicted_labels = (predictions > 0.5).float()  # Convert to binary labels\n",
        "\n",
        "                correct_predictions += (predicted_labels == targets).sum().item()\n",
        "                total_predictions += targets.numel()\n",
        "\n",
        "        avg_eval_loss = eval_loss / len(val_loader)\n",
        "        accuracy = correct_predictions / total_predictions\n",
        "\n",
        "        print(f\"Epoch {epoch + 1}/{num_epochs}, Train Loss: {avg_train_loss:.4f}, Eval Loss: {avg_eval_loss:.4f}, Accuracy: {accuracy:.4f}\")\n",
        "\n",
        "        scheduler.step()\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "WM4wYPS7O-Fg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
        "\n",
        "# Load the pre-trained ViT model and feature extractor\n",
        "feature_extractor = ViTFeatureExtractor.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n",
        "vit_model = ViTForImageClassification.from_pretrained(\"google/vit-base-patch16-224-in21k\", num_labels=40)\n",
        "vit_model.to(device)\n",
        "\n",
        "# Create datasets and dataloaders\n",
        "vit_train_dataset = ViTDataset(train_inputs, train_targets, feature_extractor)\n",
        "vit_val_dataset = ViTDataset(val_inputs, val_targets, feature_extractor)\n",
        "\n",
        "vit_train_loader = DataLoader(vit_train_dataset, batch_size=16, shuffle=True)\n",
        "vit_val_loader = DataLoader(vit_val_dataset, batch_size=16, shuffle=False)\n",
        "\n",
        "# Optimizer and scheduler\n",
        "optimizer = optim.AdamW(vit_model.parameters(), lr=1e-4, weight_decay=0.01)\n",
        "scheduler = ExponentialLR(optimizer, gamma=0.99)\n",
        "\n",
        "# Train the ViT model\n",
        "trained_vit_model = train_model(vit_model, vit_train_loader, vit_val_loader, nn.BCEWithLogitsLoss(), optimizer, scheduler)\n",
        "\n",
        "# Save the trained ViT model\n",
        "torch.save(trained_vit_model.state_dict(), \"./trained/AnoMed/vit_model.pth\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
          "referenced_widgets": [
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          ]
        },
        "id": "gljLg3BwPALU",
        "outputId": "37c5f2de-d4c0-4413-fd5c-949a243873dd"
      },
      "execution_count": null,
      "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": "6f3472d8fdfd4e889f21d1520e1dcf99"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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": "70c9ab738dd2452b8b1f455104f126af"
            }
          },
          "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": "fbdf5c443a144cc7ba6ea21b34cd8c64"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100, Train Loss: 0.2058, Eval Loss: 0.1920, Accuracy: 0.9144\n",
            "Epoch 2/100, Train Loss: 0.1735, Eval Loss: 0.1876, Accuracy: 0.9168\n",
            "Epoch 3/100, Train Loss: 0.1548, Eval Loss: 0.1911, Accuracy: 0.9155\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n",
            "\n",
            "KeyboardInterrupt\n",
            "\n"
          ]
        }
      ]
    }
  ]
}