Dog Breed Identification.ipynb

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      "language": "python",
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      "name": "python3"
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      "name": "python",
      "version": "3.7.12",
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      "file_extension": ".py"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Import Statements\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import PIL\n",
        "import os\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision.io import read_image\n",
        "from torchvision import transforms\n",
        "import torchvision.models as models\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import csv\n",
        "import random\n",
        "from transformers import AutoImageProcessor, ViTModel\n",
        "import timm\n",
        "\n",
        "# if you are running this code in Jupyter notebook\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting the seed\n",
        "SEED = 1234\n",
        "\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed(SEED)\n",
        "torch.backends.cudnn.deterministic = True"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Loading the Dataset:**"
      ],
      "metadata": {
        "id": "XIxrDoRW8BN9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the labels\n",
        "df = pd.read_csv('/kaggle/input/dog-breed-identification/labels.csv')\n",
        "\n",
        "nums = list(range(1, len(df)))\n",
        "random.shuffle(nums)\n",
        "\n",
        "# Splitting the Training, Validation and Test set\n",
        "train_df = df.loc[df.index.isin(nums[:int(len(df) * 0.7)])].reset_index().drop(columns = \"index\")\n",
        "val_df = df.loc[df.index.isin(nums[int(len(df) * 0.7):int(len(df) * 0.9)])].reset_index().drop(columns = \"index\")\n",
        "test_df = df.loc[df.index.isin(nums[int(len(df) * 0.9):int(len(df) * 1)])].reset_index().drop(columns = \"index\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:20.443691Z",
          "iopub.execute_input": "2023-05-02T10:22:20.444366Z",
          "iopub.status.idle": "2023-05-02T10:22:20.480259Z",
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        "trusted": true,
        "id": "QhFSh6SH8BN-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# General dataset visualisation\n",
        "df[\"breed\"].value_counts(ascending = True).plot(kind = \"barh\", fontsize = \"10\", title = \"Data Distribution\", figsize = (30,50))\n",
        "# plt.savefig(\"ClassDis1.png\")"
      ],
      "metadata": {
        "execution": {
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        "outputId": "9cf80617-666f-4d97-d626-bb404252181c"
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      "execution_count": null,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:title={'center':'Data Distribution'}>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 3000x5000 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Custom class to load the Data\n",
        "class CustomDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, df, images_folder, transform = None):\n",
        "        self.df = df\n",
        "        self.images_folder = images_folder\n",
        "        self.transform = transform\n",
        "\n",
        "        names= list(set(df['breed']))\n",
        "        num = [x for x in range(0,len(names))]\n",
        "        class2id = dict(zip(names,num))\n",
        "        self.class2index = class2id\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "    def __getitem__(self, index):\n",
        "        filename = self.df.loc[index][\"id\"]+'.jpg'\n",
        "        label = self.class2index[self.df.loc[index][\"breed\"]]\n",
        "        image = PIL.Image.open(os.path.join(self.images_folder, filename))\n",
        "        if self.transform is not None:\n",
        "            image = self.transform(image)\n",
        "        return image, label"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:29.863195Z",
          "iopub.execute_input": "2023-05-02T10:22:29.863667Z",
          "iopub.status.idle": "2023-05-02T10:22:29.872276Z",
          "shell.execute_reply.started": "2023-05-02T10:22:29.863626Z",
          "shell.execute_reply": "2023-05-02T10:22:29.871084Z"
        },
        "trusted": true,
        "id": "YZO2aOxd8BN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standard Transformation for the Validation and Test Set\n",
        "transformation_val_test = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:32.389821Z",
          "iopub.execute_input": "2023-05-02T10:22:32.390515Z",
          "iopub.status.idle": "2023-05-02T10:22:32.396536Z",
          "shell.execute_reply.started": "2023-05-02T10:22:32.390473Z",
          "shell.execute_reply": "2023-05-02T10:22:32.395338Z"
        },
        "trusted": true,
        "id": "CJs7VxQg8BN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the Validation and Test set\n",
        "val_dataset = CustomDataset(val_df, \"/kaggle/input/dog-breed-identification/train\", transformation_val_test)\n",
        "test_dataset = CustomDataset(test_df, \"/kaggle/input/dog-breed-identification/train\", transformation_val_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:35.913809Z",
          "iopub.execute_input": "2023-05-02T10:22:35.914971Z",
          "iopub.status.idle": "2023-05-02T10:22:35.924082Z",
          "shell.execute_reply.started": "2023-05-02T10:22:35.914920Z",
          "shell.execute_reply": "2023-05-02T10:22:35.922909Z"
        },
        "trusted": true,
        "id": "hROiP6YH8BOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "\n",
        "val_data_loader = torch.utils.data.DataLoader(val_dataset, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)\n",
        "test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:37.896527Z",
          "iopub.execute_input": "2023-05-02T10:22:37.897058Z",
          "iopub.status.idle": "2023-05-02T10:22:37.905341Z",
          "shell.execute_reply.started": "2023-05-02T10:22:37.896988Z",
          "shell.execute_reply": "2023-05-02T10:22:37.904107Z"
        },
        "trusted": true,
        "id": "Qko7EhJ18BOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Training Loop:**"
      ],
      "metadata": {
        "id": "Xg9YHfQ-8BOA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function for training the model on the Training set\n",
        "def train(epochs, model, train_data, val_data, loss_fn, opt):\n",
        "\n",
        "    model.train()\n",
        "    train_loss = []\n",
        "    train_acc = []\n",
        "    val_loss = []\n",
        "    val_acc = []\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "\n",
        "        epoch_run_loss = 0.0\n",
        "        epoch_run_acc = 0.0\n",
        "        epoch_run_loss_v = 0.0\n",
        "        epoch_run_acc_v = 0.0\n",
        "\n",
        "        for image, label in train_data:\n",
        "\n",
        "            image = image.cuda()\n",
        "            label = label.cuda()\n",
        "            # Prediction of the model given the image\n",
        "            prediction = model(image)\n",
        "\n",
        "            # Getting the Loss\n",
        "            loss = loss_fn(prediction, label)\n",
        "\n",
        "            # Loss per epoch\n",
        "            epoch_run_loss = 0.9 * epoch_run_loss + 0.1 * loss.item()\n",
        "\n",
        "            # Accuracy per epoch\n",
        "            max_value, max_index = torch.max(prediction, dim = 1)\n",
        "            epoch_run_acc += (max_index == label).sum()\n",
        "\n",
        "            # Updating the weights\n",
        "            opt.zero_grad()\n",
        "            loss.backward()\n",
        "            opt.step()\n",
        "\n",
        "        # Training Loss and Accuracy\n",
        "        epoch_train_acc = (epoch_run_acc/len(train_data.dataset)).item()\n",
        "        train_loss.append(epoch_run_loss)\n",
        "        train_acc.append(epoch_train_acc)\n",
        "\n",
        "        print(\"Traning Loss at Epoch {}: {:.4f}\".format(epoch, epoch_run_loss))\n",
        "        print(\"Traning Accuracy at Epoch {}: {:.4f}\".format(epoch, epoch_train_acc))\n",
        "\n",
        "        # Validation Loss and Accuracy\n",
        "        model.eval()\n",
        "\n",
        "        for image, label in val_data:\n",
        "            image = image.cuda()\n",
        "            label = label.cuda()\n",
        "            # Prediction of the model given the image\n",
        "            prediction = model(image)\n",
        "\n",
        "            # Getting the Loss\n",
        "            loss = loss_fn(prediction, label)\n",
        "\n",
        "            # Loss per epoch\n",
        "            epoch_run_loss_v = 0.9 * epoch_run_loss_v + 0.1 * loss.item()\n",
        "\n",
        "\n",
        "            # Accuracy per epoch\n",
        "            max_value, max_index = torch.max(prediction, dim = 1)\n",
        "            epoch_run_acc_v += (max_index == label).sum()\n",
        "\n",
        "        epoch_val_acc = 100 * ((epoch_run_acc_v/len(val_data.dataset)).item())\n",
        "        val_loss.append(epoch_run_loss_v)\n",
        "        val_acc.append(epoch_val_acc)\n",
        "\n",
        "        print(\"Validation Loss at Epoch {}: {:.4f}\".format(epoch, epoch_run_loss_v))\n",
        "        print(\"Validation Accuracy at Epoch {}: {:.4f}\".format(epoch, epoch_val_acc))\n",
        "\n",
        "    return train_loss, train_acc, val_loss, val_acc"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:40.378201Z",
          "iopub.execute_input": "2023-05-02T10:22:40.378565Z",
          "iopub.status.idle": "2023-05-02T10:22:40.392203Z",
          "shell.execute_reply.started": "2023-05-02T10:22:40.378530Z",
          "shell.execute_reply": "2023-05-02T10:22:40.390906Z"
        },
        "trusted": true,
        "id": "DrE-fxFj8BOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Evaluation Loop:**"
      ],
      "metadata": {
        "id": "AKjWLK-E8BOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function for Evaluating the model\n",
        "def test(model, test_data, loss_fn):\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    test_loss = 0.0\n",
        "    test_acc = 0.0\n",
        "\n",
        "    for image, label in test_data:\n",
        "\n",
        "        image = image.cuda()\n",
        "        label = label.cuda()\n",
        "        # Prediction of the model given the image\n",
        "        prediction = model(image)\n",
        "\n",
        "        # Getting the Loss\n",
        "        loss = loss_fn(prediction, label)\n",
        "\n",
        "        # Loss per epoch\n",
        "        test_loss += loss.item()\n",
        "\n",
        "        # Accuracy per epoch\n",
        "        max_value, max_index = torch.max(prediction, dim = 1)\n",
        "        test_acc += (max_index == label).sum()\n",
        "\n",
        "    test_loss = (test_loss / len(test_data.dataset))\n",
        "    test_acc = 100 * ((test_acc / len(test_data.dataset)).item())\n",
        "\n",
        "    return test_loss, test_acc"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:44.743042Z",
          "iopub.execute_input": "2023-05-02T10:22:44.743766Z",
          "iopub.status.idle": "2023-05-02T10:22:44.751465Z",
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          "shell.execute_reply": "2023-05-02T10:22:44.750319Z"
        },
        "trusted": true,
        "id": "NOPqramm8BOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualisation function:**"
      ],
      "metadata": {
        "id": "Y8hvBlEH8BOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plots(train_loss, validation_loss, train_accuracy, validation_accuracy):\n",
        "    epochs = []\n",
        "\n",
        "    for i in range(1):\n",
        "        epochs.append(i)\n",
        "\n",
        "    # Training and Validation Loss\n",
        "    plt.plot(epochs, train_loss, label = \"Training Loss\")\n",
        "    plt.plot(epochs, validation_loss, label = \"Validation Loss\")\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.legend()\n",
        "    plt.savefig(\"Loss.png\")\n",
        "    plt.show()\n",
        "\n",
        "    for i in range(len(train_accuracy)):\n",
        "        train_accuracy[i] = train_accuracy[i] * 100\n",
        "\n",
        "    # Training and Validation Accuracy\n",
        "    plt.plot(epochs, train_accuracy, label = \"Training Accuracy\")\n",
        "    plt.plot(epochs, validation_accuracy, label = \"Validation Accuracy\")\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend()\n",
        "    plt.savefig(\"Accuracy.png\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:51.244906Z",
          "iopub.execute_input": "2023-05-02T10:22:51.245632Z",
          "iopub.status.idle": "2023-05-02T10:22:51.253730Z",
          "shell.execute_reply.started": "2023-05-02T10:22:51.245589Z",
          "shell.execute_reply": "2023-05-02T10:22:51.252551Z"
        },
        "trusted": true,
        "id": "xshtKgdD8BOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Transfomations and Augmentations:**"
      ],
      "metadata": {
        "id": "h9e5CTz-8BOC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the Training set with the standard Transforms for Run 1\n",
        "train_dataset_run1 = CustomDataset(train_df, \"/kaggle/input/dog-breed-identification/train\", transformation_val_test)\n",
        "train_data_loader_run1 = torch.utils.data.DataLoader(train_dataset_run1, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:54.520492Z",
          "iopub.execute_input": "2023-05-02T10:22:54.521260Z",
          "iopub.status.idle": "2023-05-02T10:22:54.527870Z",
          "shell.execute_reply.started": "2023-05-02T10:22:54.521219Z",
          "shell.execute_reply": "2023-05-02T10:22:54.526738Z"
        },
        "trusted": true,
        "id": "bnlLt8DY8BOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformation and Augmentation for Run 2 - Random Rotation by a degree of 30\n",
        "transformation_run2 = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomRotation(degrees = 30),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Loading the Training set with the standard Transforms and rotation\n",
        "train_dataset_run2 = CustomDataset(train_df, \"/kaggle/input/dog-breed-identification/train\", transformation_run2)\n",
        "train_data_loader_run2 = torch.utils.data.DataLoader(train_dataset_run2, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:56.706165Z",
          "iopub.execute_input": "2023-05-02T10:22:56.706542Z",
          "iopub.status.idle": "2023-05-02T10:22:56.714356Z",
          "shell.execute_reply.started": "2023-05-02T10:22:56.706504Z",
          "shell.execute_reply": "2023-05-02T10:22:56.713096Z"
        },
        "trusted": true,
        "id": "Q0PqUofc8BOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformation and Augmentation for Run 3 - Colour Jitter\n",
        "transformation_run3 = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ColorJitter(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Loading the Training set with the standard Transforms and Colour Jitter\n",
        "train_dataset_run3 = CustomDataset(train_df, \"/kaggle/input/dog-breed-identification/train\", transformation_run3)\n",
        "train_data_loader_run3 = torch.utils.data.DataLoader(train_dataset_run3, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:22:59.491853Z",
          "iopub.execute_input": "2023-05-02T10:22:59.492567Z",
          "iopub.status.idle": "2023-05-02T10:22:59.500490Z",
          "shell.execute_reply.started": "2023-05-02T10:22:59.492526Z",
          "shell.execute_reply": "2023-05-02T10:22:59.499321Z"
        },
        "trusted": true,
        "id": "-DAq09rF8BOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformation and Augmentation for Run 4 - Random Horizontal Flip\n",
        "transformation_run4 = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Loading the Training set with the standard Transforms and Horizontal Flip\n",
        "train_dataset_run4 = CustomDataset(train_df, \"/kaggle/input/dog-breed-identification/train\", transformation_run4)\n",
        "train_data_loader_run4 = torch.utils.data.DataLoader(train_dataset_run4, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)\n",
        "\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:23:01.276448Z",
          "iopub.execute_input": "2023-05-02T10:23:01.277062Z",
          "iopub.status.idle": "2023-05-02T10:23:01.284234Z",
          "shell.execute_reply.started": "2023-05-02T10:23:01.276999Z",
          "shell.execute_reply": "2023-05-02T10:23:01.283078Z"
        },
        "trusted": true,
        "id": "HWPCYCGA8BOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformation and Augmentation for Run 5 - Crop\n",
        "transformation_run5 = transforms.Compose([\n",
        "    transforms.Resize((256, 256)),\n",
        "    transforms.CenterCrop(size = 224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Loading the Training set with the standard Transforms and Crop\n",
        "train_dataset_run5 = CustomDataset(train_df, \"/kaggle/input/dog-breed-identification/train\", transformation_run5)\n",
        "train_data_loader_run5 = torch.utils.data.DataLoader(train_dataset_run5, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:23:03.678665Z",
          "iopub.execute_input": "2023-05-02T10:23:03.679037Z",
          "iopub.status.idle": "2023-05-02T10:23:03.688241Z",
          "shell.execute_reply.started": "2023-05-02T10:23:03.678992Z",
          "shell.execute_reply": "2023-05-02T10:23:03.686313Z"
        },
        "trusted": true,
        "id": "6ocO53wM8BOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformations and Augmentation for Run 6 - Rotation, Colour Jitter, Horizontal Flip and Crop\n",
        "transformation_run6 = transforms.Compose([\n",
        "    transforms.Resize((256, 256)),\n",
        "    transforms.RandomRotation(degrees = 30),\n",
        "    transforms.ColorJitter(),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.CenterCrop(size = 224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                         [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Loading the Training set with all the Transforms\n",
        "train_dataset_run6 = CustomDataset(train_df, \"/kaggle/input/dog-breed-identification/train\", transformation_run6)\n",
        "train_data_loader_run6 = torch.utils.data.DataLoader(train_dataset_run6, batch_size = BATCH_SIZE, shuffle = True,  num_workers = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:23:06.612656Z",
          "iopub.execute_input": "2023-05-02T10:23:06.613245Z",
          "iopub.status.idle": "2023-05-02T10:23:06.621236Z",
          "shell.execute_reply.started": "2023-05-02T10:23:06.613203Z",
          "shell.execute_reply": "2023-05-02T10:23:06.619984Z"
        },
        "trusted": true,
        "id": "2CUFFCn78BOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining the loss function\n",
        "loss = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-05-02T10:23:09.210428Z",
          "iopub.execute_input": "2023-05-02T10:23:09.210813Z",
          "iopub.status.idle": "2023-05-02T10:23:09.216196Z",
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          "shell.execute_reply": "2023-05-02T10:23:09.215072Z"
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        "trusted": true,
        "id": "20vVzf638BOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. VGG16:**"
      ],
      "metadata": {
        "id": "euMLh4S-8BOG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading VGG16 with the pre-trained weights\n",
        "vgg_16 = models.vgg16(pretrained = True)\n",
        "\n",
        "# Changing the Last Fully Connected layer from 1000 classes to 120 classes\n",
        "vgg_16.classifier[6].out_features = 120\n",
        "\n",
        "# We will use the pre-trained weights for the Convolution layers\n",
        "for param in vgg_16.features.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "vgg_16.cuda()\n",
        "\n",
        "# Defining the loss function\n",
        "loss = nn.CrossEntropyLoss()\n",
        "\n",
        "# Defining the optimiser as Stochastic Gradient Descent\n",
        "optimiser = optim.SGD(vgg_16.classifier.parameters(), lr = 0.0001, momentum = 0.9)"
      ],
      "metadata": {
        "trusted": true,
        "id": "HE29KsWz8BOG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.1. VGG16 - Standard Tranformations:**"
      ],
      "metadata": {
        "id": "qk3ZZa4V8BOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "train_loss, train_accuracy, validation_loss, validation_accuracy = train(10, vgg_16, train_data_loader_run1, val_data_loader, loss, optimiser)\n",
        "\n",
        "# Visualisation\n",
        "plots(train_loss, validation_loss, train_accuracy, validation_accuracy)\n",
        "\n",
        "# Accuracy and Loss on the Test set\n",
        "test_loss, test_accuracy = test(vgg_16, test_data_loader, loss)\n",
        "print(\"Test Loss = \", test_loss)\n",
        "print(\"Test Accuracy = \", test_accuracy)"
      ],
      "metadata": {
        "trusted": true,
        "id": "f2qk0lNc8BOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.2. VGG16 - Rotation:**"
      ],
      "metadata": {
        "id": "HnrzRjXZ8BOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "train_loss, train_accuracy, validation_loss, validation_accuracy = train(10, vgg_16, train_data_loader_run2, val_data_loader, loss, optimiser)\n",
        "\n",
        "# Visualisation\n",
        "plots(train_loss, validation_loss, train_accuracy, validation_accuracy)\n",
        "\n",
        "# Accuracy and Loss on the Test set\n",
        "test_loss, test_accuracy = test(vgg_16, test_data_loader, loss)\n",
        "print(\"Test Loss = \", test_loss)\n",
        "print(\"Test Accuracy = \", test_accuracy)"
      ],
      "metadata": {
        "trusted": true,
        "id": "b1W7ubtk8BOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.3. VGG16 - Colour Jitter:**"
      ],
      "metadata": {
        "id": "TWkUO1PI8BOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "train_loss, train_accuracy, validation_loss, validation_accuracy = train(10, vgg_16, train_data_loader_run3, val_data_loader, loss, optimiser)\n",
        "\n",
        "# Visualisation\n",
        "plots(train_loss, validation_loss, train_accuracy, validation_accuracy)\n",
        "\n",
        "# Accuracy and Loss on the Test set\n",
        "test_loss, test_accuracy = test(vgg_16, test_data_loader, loss)\n",
        "print(\"Test Loss = \", test_loss)\n",
        "print(\"Test Accuracy = \", test_accuracy)"
      ],
      "metadata": {
        "trusted": true,
        "id": "dsMwdDU78BOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.4. VGG16 - Horizontal Flip:**"
      ],
      "metadata": {
        "id": "u9Xezcpy8BOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "train_loss, train_accuracy, validation_loss, validation_accuracy = train(10, vgg_16, train_data_loader_run4, val_data_loader, loss, optimiser)\n",
        "\n",
        "# Visualisation\n",
        "plots(train_loss, validation_loss, train_accuracy, validation_accuracy)\n",
        "\n",
        "# Accuracy and Loss on the Test set\n",
        "test_loss, test_accuracy = test(vgg_16, test_data_loader, loss)\n",
        "print(\"Test Loss = \", test_loss)\n",
        "print(\"Test Accuracy = \", test_accuracy)"
      ],
      "metadata": {
        "trusted": true,
        "id": "cxdjC7kG8BOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.5. VGG16 - Crop:**"
      ],
      "metadata": {
        "id": "aPmrC2Zn8BOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "train_loss, train_accuracy, validation_loss, validation_accuracy = train(10, vgg_16, train_data_loader_run5, val_data_loader, loss, optimiser)\n",
        "\n",
        "# Visualisation\n",
        "plots(train_loss, validation_loss, train_accuracy, validation_accuracy)\n",
        "\n",
        "# Accuracy and Loss on the Test set\n",
        "test_loss, test_accuracy = test(vgg_16, test_data_loader, loss)\n",
        "print(\"Test Loss = \", test_loss)\n",
        "print(\"Test Accuracy = \", test_accuracy)"
      ],
      "metadata": {
        "trusted": true,
        "id": "h161QDs68BOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.6. VGG16 - Rotation, Colour Jitter, Horizontal Flip, Crop:**"
      ],
      "metadata": {
        "id": "PZS7yzV98BOI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "train_loss, train_accuracy, validation_loss, validation_accuracy = train(10, vgg_16, train_data_loader_run6, val_data_loader, loss, optimiser)\n",
        "\n",
        "# Visualisation\n",
        "plots(train_loss, validation_loss, train_accuracy, validation_accuracy)\n",
        "\n",
        "# Accuracy and Loss on the Test set\n",
        "test_loss, test_accuracy = test(vgg_16, test_data_loader, loss)\n",
        "print(\"Test Loss = \", test_loss)\n",
        "print(\"Test Accuracy = \", test_accuracy)"
      ],
      "metadata": {
        "trusted": true,
        "id": "PpAm-e6X8BOI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Vision Transformer (ViT):**"
      ],
      "metadata": {
        "id": "cHEzxAUF8BOJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def patch_image(images, patch_size):\n",
        "    # Image shape => (32, 3, 224, 224)\n",
        "    batch = images.shape[0]\n",
        "    channel = images.shape[1]\n",
        "    height = images.shape[2]\n",
        "    width = images.shape[3]\n",
        "\n",
        "    # Each patch is of the size 16x16\n",
        "    patch_height = patch_size[0]\n",
        "    patch_width = patch_size[1]\n",
        "    # There are a total of 14x14 = 196 patches per image\n",
        "    num_patch_h = height // patch_height\n",
        "    num_patch_w = width // patch_width\n",
        "\n",
        "    # Each image has 196 images, and the standard tranformer takes an embedding vector of 768\n",
        "    # This is the flattened output we are trying to get\n",
        "    # Output shape => (32, 196, 768)\n",
        "    batch_patch = torch.reshape(images, (batch, channel, num_patch_h, patch_height, num_patch_w, patch_width))\n",
        "    batch_patch = torch.permute(batch_patch, (0, 1, 2, 4, 3, 5))\n",
        "    batch_patch = torch.permute(batch_patch, (0, 2, 3, 4, 5, 1))\n",
        "    batch_patch = torch.reshape(images, (batch, num_patch_h * num_patch_w, channel * patch_height * patch_width))\n",
        "\n",
        "    return batch_patch"
      ],
      "metadata": {
        "trusted": true,
        "id": "orchsjux8BOJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ViT(nn.Module):\n",
        "\n",
        "    def __init__(self, img_size, patch_size, emb_dim, num_heads, dim_feedforward, blocks, num_classes):\n",
        "        super(ViT, self).__init__()\n",
        "\n",
        "        # img_size = image size with (channels, height, width)\n",
        "        # patch_size = Dimensions of each patch in an image\n",
        "        # emd_dim = Linear tranformation value\n",
        "        # num_heads = number of heads for the multi head attention models\n",
        "        # dim_feedforward = Dimensions of the feed forward network (2048 is default - pytorch)\n",
        "        self.img_size = img_size\n",
        "        self.patch_size = patch_size\n",
        "        n_p = (img_size[1] // patch_size[0]) * (img_size[2] // patch_size[1])\n",
        "\n",
        "        token_size = patch_size[0] * patch_size[1] * img_size[0]\n",
        "        self.linear = nn.Linear(token_size, emb_dim)\n",
        "\n",
        "        # Classification token with the shape => (1, 1, 196)\n",
        "        self.c_token = nn.Parameter(torch.randn(1, 1, emb_dim))\n",
        "\n",
        "        # Positional Emdedding\n",
        "        self.pos_emb = nn.Parameter(torch.randn(n_p, emb_dim))\n",
        "\n",
        "        # Transfomer model in pytorch\n",
        "        encoder_layer = nn.TransformerEncoderLayer(emb_dim, num_heads, dim_feedforward, activation = \"gelu\", batch_first = True)\n",
        "        self.transformer = nn.TransformerEncoder(encoder_layer, blocks)\n",
        "\n",
        "        self.mlp = nn.Sequential(nn.Linear(emb_dim, num_classes), nn.Softmax(dim = -1))\n",
        "\n",
        "    def forward(self, images):\n",
        "\n",
        "        batch = images.shape[0]\n",
        "        channel = images.shape[1]\n",
        "        height = images.shape[2]\n",
        "        width = images.shape[3]\n",
        "\n",
        "        # patch shape => (32, 196, 768)\n",
        "        patches = patch_image(images, self.patch_size)\n",
        "\n",
        "        # Linear Layer, shape => (32, 196, 196)\n",
        "        tokens = self.linear(patches)\n",
        "\n",
        "        # Adding the Classification token\n",
        "        # Shape => (32, 1, 196)\n",
        "        c_token = self.c_token.expand([batch, -1, -1])\n",
        "\n",
        "        # Positional Embedding shape => (32, 196, 196)\n",
        "        embedding = tokens + self.pos_emb\n",
        "\n",
        "        # Concatenate the classification token with other embedding\n",
        "        # Shape => (32, 197, 196)\n",
        "        embedding = torch.cat([c_token, tokens], axis = 1)\n",
        "\n",
        "        # Passing the embedding to the Transformer\n",
        "        # transformer gives the same shape as the input\n",
        "        # shape => (32, 197, 196)\n",
        "        transformer = self.transformer(embedding)\n",
        "\n",
        "        # Classification head which outputs the probability of each class\n",
        "        # This is only done with the classification token at the top of the embedding\n",
        "        # classification_token, shape => (32, 1, 196)\n",
        "        classification_token = transformer[:,0,:]\n",
        "\n",
        "        # Shape => (32, 120)\n",
        "        classification = self.mlp(classification_token)\n",
        "        result = torch.argmax(classification, dim = 1)\n",
        "\n",
        "        return classification"
      ],
      "metadata": {
        "trusted": true,
        "id": "2b_MZGWh8BOJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = ViT(\n",
        "    img_size=(3, 224, 224),\n",
        "    patch_size = (16, 16),\n",
        "    emb_dim = 196,\n",
        "    num_heads = 4,\n",
        "    dim_feedforward = 2048,\n",
        "    blocks = 8,\n",
        "    num_classes = 120\n",
        "  )\n",
        "model.cuda()"
      ],
      "metadata": {
        "trusted": true,
        "id": "CTEZKUqf8BOJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "train_loss, train_accuracy, validation_loss, validation_accuracy = train(2, model, train_data_loader_run4, val_data_loader, loss, optimiser)\n",
        "\n",
        "# Visualisation\n",
        "plots(train_loss, validation_loss, train_accuracy, validation_accuracy)\n",
        "\n",
        "# Accuracy and Loss on the Test set\n",
        "test_loss, test_accuracy = test(model, test_data_loader, loss)\n",
        "print(\"Test Loss = \", test_loss)\n",
        "print(\"Test Accuracy = \", test_accuracy)"
      ],
      "metadata": {
        "trusted": true,
        "id": "X48bFRua8BOJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}