{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kMhcJCI6Uj_"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "# Session 13"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ah8S0cE-6UkB"
      },
      "source": [
        "## Cloning the repo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLjZq7mkdD3c",
        "outputId": "5ed6950c-d45e-4004-f80f-31bbbb2ed6c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahgXUcildKP4",
        "outputId": "714e2d3f-eb32-443f-aa28-75b9692f0460"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/S13\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/S13/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "repo_name = \"erav2s13\"\n",
        "git_repo_url = \"https://huggingface.co/spaces/akutty/erav2s13\"\n",
        "branch_name= \"main\"\n"
      ],
      "metadata": {
        "id": "ZcwD0tT1ew25"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf \"{repo_name}\" && ! git clone \"{git_repo_url}\"\n",
        "    # Switch to repo folder, Needed to switch branch\n",
        "    # Switch branch and change to the correct directory\n",
        "!cd \"{repo_name}\" && git checkout {branch_name}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exSr5ZXCfGVA",
        "outputId": "3f8b1f94-788d-4438-eda9-11cab584fc34"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'erav2s13'...\n",
            "remote: Enumerating objects: 60, done.\u001b[K\n",
            "remote: Counting objects: 100% (56/56), done.\u001b[K\n",
            "remote: Compressing objects: 100% (55/55), done.\u001b[K\n",
            "remote: Total 60 (delta 26), reused 0 (delta 0), pack-reused 4\u001b[K\n",
            "Unpacking objects: 100% (60/60), 616.96 KiB | 442.00 KiB/s, done.\n",
            "Already on 'main'\n",
            "Your branch is up to date with 'origin/main'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qImGSLaJcqGB",
        "outputId": "8c176907-0eda-4546-95d6-94f3c5ff8f69"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \u001b[0m\u001b[01;34mcheckpoints\u001b[0m/   \u001b[01;34mdata\u001b[0m/   \u001b[01;34merav2s13\u001b[0m/   \u001b[01;34mlogs\u001b[0m/  'main(1).ipynb'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TR4Jzy1Lge7v",
        "outputId": "70526160-14fc-4b0e-8e21-d9a9fb067f7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/S13/erav2s13\n"
          ]
        }
      ],
      "source": [
        "%cd erav2s13\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJc_ls7VcvVn"
      },
      "source": [
        "# CIFAR-10 dataset classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BD00g_s0cvVp"
      },
      "source": [
        "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n",
        "\n",
        "The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkVIX0wGcvVq"
      },
      "source": [
        "# Importing basic dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "wNTxi7H2g-4X"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "    # https://lightning.ai/docs/pytorch/stable/\n",
        "!{sys.executable} -m pip install -q lightning\n",
        "    # https://github.com/davidtvs/pytorch-lr-finder\n",
        "!{sys.executable} -m pip install -q torch-lr-finder\n",
        "    # https://github.com/tyleryep/torchinfo\n",
        "!{sys.executable} -m pip install -q torchinfo\n",
        "    # https://github.com/jacobgil/pytorch-grad-cam\n",
        "!{sys.executable} -m pip install -q grad-cam\n",
        "    # https://www.gradio.app/\n",
        "!{sys.executable} -m pip install -q gradio\n",
        "    # https://github.com/tensorflow/tensorboard\n",
        "!{sys.executable} -m pip install -q tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "VQCGmLdrcvVq"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from lightning_fabric.utilities.seed import seed_everything\n",
        "from pytorch_lightning.loggers import TensorBoardLogger\n",
        "import tensorboard\n",
        "from tqdm.auto import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%ls"
      ],
      "metadata": {
        "id": "x1OGRYzfk2Oc",
        "outputId": "991c11c3-60b7-460e-9be1-732c33bbdc6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "app.py  \u001b[0m\u001b[01;34mmodels\u001b[0m/  \u001b[01;34mmodules\u001b[0m/  README.md  S13.ipynb\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import user defined modules\n",
        "import modules.config as config\n",
        "from models.custom_resnet import CustomResNet as Net\n",
        "from models.custom_resnet import detailed_model_summary\n",
        "from modules.dataset import CIFAR_CLASSES\n",
        "from modules.lightning_dataset import CIFARDataModule\n",
        "from modules.trainer import train_and_test_model\n",
        "from modules.utils import get_num_workers, pretty_print_metrics\n",
        "from modules.visualize import (\n",
        "    plot_gradcam_images,\n",
        "    plot_misclassified_images,\n",
        "    plot_sample_training_images,\n",
        "    plot_train_test_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "NvevXAs3dehA"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = config.DATA_PATH\n",
        "checkpoint_path = config.CHECKPOINT_PATH\n",
        "logging_path = config.LOGGING_PATH\n",
        "\n",
        "# Specify the number of epochs\n",
        "num_epochs = config.NUM_EPOCHS\n",
        "\n",
        "# Set the batch size\n",
        "batch_size = config.BATCH_SIZE\n",
        "\n",
        "# Set seed value for reproducibility\n",
        "seed = config.SEED"
      ],
      "metadata": {
        "id": "n_1iLyWTlKBK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_run_location = \"colab\"\n",
        "num_workers = get_num_workers(model_run_location)\n",
        "\n",
        "# https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
        "torch.set_float32_matmul_precision(\"medium\")\n",
        "\n",
        "print(\n",
        "    f\"Run location: {model_run_location}, \\n\",\n",
        "    f\"Number of workers: {num_workers}, \\n\",\n",
        "    f\"Batch size: {batch_size} \\n\",\n",
        "    f\"Seed: {seed} \\n\",\n",
        "    f\"Data path: {data_path} \\n\",\n",
        "    f\"Logging path: {logging_path} \\n\",\n",
        "    f\"Checkpoint path: {checkpoint_path}\",\n",
        ")\n",
        "\n",
        "seed_everything(seed)\n",
        "\n"
      ],
      "metadata": {
        "id": "LnDKLmJ7lcN_",
        "outputId": "128b6fef-4908-4a11-a453-131f9fcc687c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:lightning_fabric.utilities.seed:Seed set to 53\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Run location: colab, \n",
            " Number of workers: 2, \n",
            " Batch size: 512 \n",
            " Seed: 53 \n",
            " Data path: ../../data/ \n",
            " Logging path: ../../logs/ \n",
            " Checkpoint path: ../../checkpoints/\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cifar_data_module = CIFARDataModule(data_path=data_path, batch_size=batch_size, seed=seed, num_workers=num_workers)\n",
        "# cifar_data_module = CIFARDataModule(data_path=data_path, batch_size=batch_size, seed=seed, num_workers=0)\n",
        "cifar_data_module.prepare_data()\n",
        "cifar_data_module.setup()\n",
        "\n",
        "# Extract the train and test dataloaders\n",
        "# train_loader = cifar_data_module.training_dataset\n",
        "# val_loader = cifar_data_module.validation_dataset\n",
        "# test_loader = cifar_data_module.testing_dataset\n",
        "\n",
        "train_loader = cifar_data_module.train_dataloader()\n",
        "val_loader = cifar_data_module.val_dataloader()\n",
        "test_loader = cifar_data_module.test_dataloader()\n",
        "\n",
        "# Get class mapping for the dataset\n",
        "classes = CIFAR_CLASSES\n",
        "print(f\"Class Labels: {classes}\")\n"
      ],
      "metadata": {
        "id": "oJNjXbZxmM09",
        "outputId": "800e42c8-b69c-4c9f-aec0-93277a35c4a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Class Labels: ('Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_data, batch_label = next(iter(train_loader))\n",
        "\n",
        "# Plot 30 sample images from the training data along with their labels\n",
        "# plot_sample_training_images() imported from utils.py\n",
        "fig, axs = plot_sample_training_images(batch_data, batch_label, class_label=classes, num_images=30)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fMac-D_8nfDF",
        "outputId": "fa78e9d6-211a-4f3d-9f50-5fde9a20aa81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model\n",
        "# No need to send the model to device as we are using the LightningModule\n",
        "model = Net()\n",
        "\n",
        "# enable printing shape\n",
        "model.print_shape = True\n",
        "\n",
        "# # Print the model summary by specifying the input size\n",
        "# summary(model, input_size=(3, 32, 32))\n",
        "detailed_model_summary(model, input_size=(3, 32, 32))\n",
        "\n",
        "# disable printing shape for cleaner test train output\n",
        "model.print_shape = False"
      ],
      "metadata": {
        "id": "MwpdQ9rWnp8X",
        "outputId": "c67a1ea6-1bfb-4e16-ff20-c24ad1185e34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PrepLayer \n",
            "\t torch.Size([1, 64, 32, 32]) \n",
            "\n",
            "Layer 1, X \n",
            "\t torch.Size([1, 128, 16, 16]) \n",
            "\n",
            "Layer 1, R1 \n",
            "\t torch.Size([1, 128, 16, 16]) \n",
            "\n",
            "Layer 1, X + R1 \n",
            "\t torch.Size([1, 128, 16, 16]) \n",
            "\n",
            "Layer 2 \n",
            "\t torch.Size([1, 256, 8, 8]) \n",
            "\n",
            "Layer 3, X \n",
            "\t torch.Size([1, 512, 4, 4]) \n",
            "\n",
            "Layer 3, R2 \n",
            "\t torch.Size([1, 512, 4, 4]) \n",
            "\n",
            "Layer 3, X + R2 \n",
            "\t torch.Size([1, 512, 4, 4]) \n",
            "\n",
            "Max Pooling \n",
            "\t torch.Size([1, 512, 1, 1]) \n",
            "\n",
            "Reshape before FC \n",
            "\t torch.Size([1, 512]) \n",
            "\n",
            "After FC \n",
            "\t torch.Size([1, 10]) \n",
            "\n",
            "========================================================================================================================\n",
            "Layer (type:depth-idx)                   Input Shape      Kernel Shape     Output Shape     Param #          Trainable\n",
            "========================================================================================================================\n",
            "CustomResNet                             [1, 3, 32, 32]   --               [1, 10]          --               True\n",
            "├─Sequential: 1-1                        [1, 3, 32, 32]   --               [1, 64, 32, 32]  --               True\n",
            "│    └─Conv2d: 2-1                       [1, 3, 32, 32]   [3, 3]           [1, 64, 32, 32]  1,728            True\n",
            "│    └─BatchNorm2d: 2-2                  [1, 64, 32, 32]  --               [1, 64, 32, 32]  128              True\n",
            "│    └─ReLU: 2-3                         [1, 64, 32, 32]  --               [1, 64, 32, 32]  --               --\n",
            "│    └─Dropout: 2-4                      [1, 64, 32, 32]  --               [1, 64, 32, 32]  --               --\n",
            "├─Sequential: 1-2                        [1, 64, 32, 32]  --               [1, 128, 16, 16] --               True\n",
            "│    └─Conv2d: 2-5                       [1, 64, 32, 32]  [3, 3]           [1, 128, 32, 32] 73,728           True\n",
            "│    └─MaxPool2d: 2-6                    [1, 128, 32, 32] 2                [1, 128, 16, 16] --               --\n",
            "│    └─BatchNorm2d: 2-7                  [1, 128, 16, 16] --               [1, 128, 16, 16] 256              True\n",
            "│    └─ReLU: 2-8                         [1, 128, 16, 16] --               [1, 128, 16, 16] --               --\n",
            "│    └─Dropout: 2-9                      [1, 128, 16, 16] --               [1, 128, 16, 16] --               --\n",
            "├─Sequential: 1-3                        [1, 128, 16, 16] --               [1, 128, 16, 16] --               True\n",
            "│    └─Conv2d: 2-10                      [1, 128, 16, 16] [3, 3]           [1, 128, 16, 16] 147,456          True\n",
            "│    └─BatchNorm2d: 2-11                 [1, 128, 16, 16] --               [1, 128, 16, 16] 256              True\n",
            "│    └─ReLU: 2-12                        [1, 128, 16, 16] --               [1, 128, 16, 16] --               --\n",
            "│    └─Dropout: 2-13                     [1, 128, 16, 16] --               [1, 128, 16, 16] --               --\n",
            "│    └─Conv2d: 2-14                      [1, 128, 16, 16] [3, 3]           [1, 128, 16, 16] 147,456          True\n",
            "│    └─BatchNorm2d: 2-15                 [1, 128, 16, 16] --               [1, 128, 16, 16] 256              True\n",
            "│    └─ReLU: 2-16                        [1, 128, 16, 16] --               [1, 128, 16, 16] --               --\n",
            "│    └─Dropout: 2-17                     [1, 128, 16, 16] --               [1, 128, 16, 16] --               --\n",
            "├─Sequential: 1-4                        [1, 128, 16, 16] --               [1, 256, 8, 8]   --               True\n",
            "│    └─Conv2d: 2-18                      [1, 128, 16, 16] [3, 3]           [1, 256, 16, 16] 294,912          True\n",
            "│    └─MaxPool2d: 2-19                   [1, 256, 16, 16] 2                [1, 256, 8, 8]   --               --\n",
            "│    └─BatchNorm2d: 2-20                 [1, 256, 8, 8]   --               [1, 256, 8, 8]   512              True\n",
            "│    └─ReLU: 2-21                        [1, 256, 8, 8]   --               [1, 256, 8, 8]   --               --\n",
            "│    └─Dropout: 2-22                     [1, 256, 8, 8]   --               [1, 256, 8, 8]   --               --\n",
            "├─Sequential: 1-5                        [1, 256, 8, 8]   --               [1, 512, 4, 4]   --               True\n",
            "│    └─Conv2d: 2-23                      [1, 256, 8, 8]   [3, 3]           [1, 512, 8, 8]   1,179,648        True\n",
            "│    └─MaxPool2d: 2-24                   [1, 512, 8, 8]   2                [1, 512, 4, 4]   --               --\n",
            "│    └─BatchNorm2d: 2-25                 [1, 512, 4, 4]   --               [1, 512, 4, 4]   1,024            True\n",
            "│    └─ReLU: 2-26                        [1, 512, 4, 4]   --               [1, 512, 4, 4]   --               --\n",
            "│    └─Dropout: 2-27                     [1, 512, 4, 4]   --               [1, 512, 4, 4]   --               --\n",
            "├─Sequential: 1-6                        [1, 512, 4, 4]   --               [1, 512, 4, 4]   --               True\n",
            "│    └─Conv2d: 2-28                      [1, 512, 4, 4]   [3, 3]           [1, 512, 4, 4]   2,359,296        True\n",
            "│    └─BatchNorm2d: 2-29                 [1, 512, 4, 4]   --               [1, 512, 4, 4]   1,024            True\n",
            "│    └─ReLU: 2-30                        [1, 512, 4, 4]   --               [1, 512, 4, 4]   --               --\n",
            "│    └─Dropout: 2-31                     [1, 512, 4, 4]   --               [1, 512, 4, 4]   --               --\n",
            "│    └─Conv2d: 2-32                      [1, 512, 4, 4]   [3, 3]           [1, 512, 4, 4]   2,359,296        True\n",
            "│    └─BatchNorm2d: 2-33                 [1, 512, 4, 4]   --               [1, 512, 4, 4]   1,024            True\n",
            "│    └─ReLU: 2-34                        [1, 512, 4, 4]   --               [1, 512, 4, 4]   --               --\n",
            "│    └─Dropout: 2-35                     [1, 512, 4, 4]   --               [1, 512, 4, 4]   --               --\n",
            "├─MaxPool2d: 1-7                         [1, 512, 4, 4]   4                [1, 512, 1, 1]   --               --\n",
            "├─Linear: 1-8                            [1, 512]         --               [1, 10]          5,130            True\n",
            "========================================================================================================================\n",
            "Total params: 6,573,130\n",
            "Trainable params: 6,573,130\n",
            "Non-trainable params: 0\n",
            "Total mult-adds (M): 379.27\n",
            "========================================================================================================================\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 4.65\n",
            "Params size (MB): 26.29\n",
            "Estimated Total Size (MB): 30.96\n",
            "========================================================================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logger = TensorBoardLogger(save_dir=logging_path, name=\"lightning_logs\", log_graph=False)"
      ],
      "metadata": {
        "id": "qEibWlxVnzWs"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "misclassified_image_data = {\"images\": [], \"ground_truths\": [], \"predicted_vals\": []}\n",
        "\n",
        "# Run the model for num_epochs\n",
        "trainer, results, misclassified_image_data = train_and_test_model(\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=num_epochs,\n",
        "    model=model,\n",
        "    datamodule=cifar_data_module,\n",
        "    logger=logger,\n",
        "    # Alert: Change this when running in production\n",
        "    debug=False,\n",
        ")"
      ],
      "metadata": {
        "id": "LjCsL5mmoA0W",
        "outputId": "9698cdcc-a856-4c37-ccb8-2526da2934e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Batch size: 512, Total epochs: 24\n",
            "\n",
            "\n",
            "Defining Lightning Callbacks\n",
            "Defining Lightning Trainer\n"
          ]
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:563: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Using 16bit Automatic Mixed Precision (AMP)\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.model_summary.ModelSummary'>]. Skipping setting a default `ModelSummary` callback.\n",
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finding the optimal learning rate using Lightning Tuner.\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:653: Checkpoint directory /content/drive/MyDrive/checkpoints exists and is not empty.\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Loading `train_dataloader` to estimate number of stepping batches.\n",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n",
            "  warnings.warn(\"The verbose parameter is deprecated. Please use get_last_lr() \"\n"
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              "Finding best initial lr:   0%|          | 0/200 [00:00<?, ?it/s]"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:143: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
            "INFO:pytorch_lightning.tuner.lr_finder:LR finder stopped early after 4 steps due to diverging loss.\n",
            "ERROR:pytorch_lightning.tuner.lr_finder:Failed to compute suggestion for learning rate because there are not enough points. Increase the loop iteration limits or the size of your dataset/dataloader.\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Restoring states from the checkpoint path at /content/drive/MyDrive/S13/erav2s13/.lr_find_899908d2-406a-49ff-9635-fbdfbd102cb6.ckpt\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Restored all states from the checkpoint at /content/drive/MyDrive/S13/erav2s13/.lr_find_899908d2-406a-49ff-9635-fbdfbd102cb6.ckpt\n"
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            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
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            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=24` reached.\n"
          ]
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        {
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            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
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        {
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          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
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          "text": [
            "Test ended! Saving misclassified images\n"
          ]
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          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    91.5199966430664     \u001b[0m\u001b[35m \u001b[0m│\n",
              "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.2885625660419464    \u001b[0m\u001b[35m \u001b[0m│\n",
              "└───────────────────────────┴───────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     91.5199966430664      </span>│\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.2885625660419464     </span>│\n",
              "└───────────────────────────┴───────────────────────────┘\n",
              "</pre>\n"
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          "text": [
            "Collecting epoch level model results.\n",
            "Collecting misclassified images.\n",
            "Saving the model.\n",
            "Model saved to ../../CustomResNet.pt\n",
            "Folder path: ../..\n",
            "Saving first 20 misclassified images.\n",
            "Folder path: ../..\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yl2trsdrcvVs"
      },
      "source": [
        "# Model Initialization & Summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "0wJukFuT5Qh5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94f70d08-50cd-4540-b656-2b7c14604560"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'dict'>\n",
            "{'train_loss': [21.73940086364746, 1.597331166267395, 1.1019726991653442, 0.9527544379234314, 0.8206701874732971, 0.7856320738792419, 0.7091265320777893, 0.6186497211456299, 0.5599020719528198, 0.4926811456680298, 0.45813995599746704, 0.4195902943611145, 0.381695032119751, 0.35549095273017883, 0.31980037689208984, 0.28752562403678894, 0.26203039288520813, 0.23159083724021912, 0.2074798196554184, 0.18456079065799713, 0.1617870181798935, 0.13749951124191284, 0.1192043349146843, 0.09911727905273438, 0.0906842052936554], 'train_acc': [9.66796875, 43.073997497558594, 60.913997650146484, 66.41799926757812, 71.15999603271484, 72.43399810791016, 75.34600067138672, 78.3759994506836, 80.45800018310547, 82.73999786376953, 83.79999542236328, 85.26599884033203, 86.42599487304688, 87.48600006103516, 88.68399810791016, 89.78599548339844, 90.65599822998047, 91.82999420166016, 92.65599822998047, 93.45199584960938, 94.39199829101562, 95.20399475097656, 95.9219970703125, 96.73799896240234, 97.0479965209961], 'test_loss': [2.303933620452881, nan, 2.303412675857544, 1.390668272972107, 0.9199668765068054, 0.7821179628372192, 0.7352397441864014, 0.7620256543159485, 0.5754162669181824, 0.6533044576644897, 0.4573260545730591, 0.48411238193511963, 0.40672019124031067, 0.43124067783355713, 0.40949755907058716, 0.3819332718849182, 0.3976947069168091, 0.3778134286403656, 0.3573845624923706, 0.31820499897003174, 0.3335122764110565, 0.32146593928337097, 0.3377351462841034, 0.3010645806789398, 0.3057439923286438, 0.2904627323150635, 0.2885625660419464], 'test_acc': [10.0, 10.0, 10.15625, 54.57999801635742, 67.93000030517578, 73.2699966430664, 75.69999694824219, 75.7699966430664, 80.40999603271484, 79.06999969482422, 85.15999603271484, 84.0199966430664, 86.48999786376953, 86.22000122070312, 87.22999572753906, 88.00999450683594, 87.29999542236328, 88.54999542236328, 88.66999816894531, 89.88999938964844, 89.93000030517578, 90.33999633789062, 89.81999969482422, 90.87999725341797, 91.32999420166016, 91.65999603271484, 91.5199966430664], 'val_loss': [], 'val_acc': []}\n",
            "dict_keys(['train_loss', 'train_acc', 'test_loss', 'test_acc', 'val_loss', 'val_acc'])\n",
            "[21.73940086364746, 1.597331166267395, 1.1019726991653442, 0.9527544379234314, 0.8206701874732971, 0.7856320738792419, 0.7091265320777893, 0.6186497211456299, 0.5599020719528198, 0.4926811456680298, 0.45813995599746704, 0.4195902943611145, 0.381695032119751, 0.35549095273017883, 0.31980037689208984, 0.28752562403678894, 0.26203039288520813, 0.23159083724021912, 0.2074798196554184, 0.18456079065799713, 0.1617870181798935, 0.13749951124191284, 0.1192043349146843, 0.09911727905273438, 0.0906842052936554]\n"
          ]
        }
      ],
      "source": [
        "print(type(results))\n",
        "print(results)\n",
        "print(results.keys())\n",
        "print(results[\"train_loss\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Plotting accuracy and loss graphs.\")\n",
        "fig, axs = plot_train_test_metrics(results)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "flFuEbauBa8L",
        "outputId": "a8cbbe83-6a12-4007-c6c2-28583215f52b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plotting accuracy and loss graphs.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_print_metrics(num_epochs=num_epochs, results=results)"
      ],
      "metadata": {
        "id": "VMjq3WxrBe5W",
        "outputId": "95812aca-b921-4849-9873-b942e9f9e40b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01, Train Loss: 21.7394, Test Loss: 2.3039, Train Accuracy: 9.6680, Test Accuracy: 10.0000\n",
            "Epoch: 02, Train Loss: 1.5973, Test Loss: nan, Train Accuracy: 43.0740, Test Accuracy: 10.0000\n",
            "Epoch: 03, Train Loss: 1.1020, Test Loss: 2.3034, Train Accuracy: 60.9140, Test Accuracy: 10.1562\n",
            "Epoch: 04, Train Loss: 0.9528, Test Loss: 1.3907, Train Accuracy: 66.4180, Test Accuracy: 54.5800\n",
            "Epoch: 05, Train Loss: 0.8207, Test Loss: 0.9200, Train Accuracy: 71.1600, Test Accuracy: 67.9300\n",
            "Epoch: 06, Train Loss: 0.7856, Test Loss: 0.7821, Train Accuracy: 72.4340, Test Accuracy: 73.2700\n",
            "Epoch: 07, Train Loss: 0.7091, Test Loss: 0.7352, Train Accuracy: 75.3460, Test Accuracy: 75.7000\n",
            "Epoch: 08, Train Loss: 0.6186, Test Loss: 0.7620, Train Accuracy: 78.3760, Test Accuracy: 75.7700\n",
            "Epoch: 09, Train Loss: 0.5599, Test Loss: 0.5754, Train Accuracy: 80.4580, Test Accuracy: 80.4100\n",
            "Epoch: 10, Train Loss: 0.4927, Test Loss: 0.6533, Train Accuracy: 82.7400, Test Accuracy: 79.0700\n",
            "Epoch: 11, Train Loss: 0.4581, Test Loss: 0.4573, Train Accuracy: 83.8000, Test Accuracy: 85.1600\n",
            "Epoch: 12, Train Loss: 0.4196, Test Loss: 0.4841, Train Accuracy: 85.2660, Test Accuracy: 84.0200\n",
            "Epoch: 13, Train Loss: 0.3817, Test Loss: 0.4067, Train Accuracy: 86.4260, Test Accuracy: 86.4900\n",
            "Epoch: 14, Train Loss: 0.3555, Test Loss: 0.4312, Train Accuracy: 87.4860, Test Accuracy: 86.2200\n",
            "Epoch: 15, Train Loss: 0.3198, Test Loss: 0.4095, Train Accuracy: 88.6840, Test Accuracy: 87.2300\n",
            "Epoch: 16, Train Loss: 0.2875, Test Loss: 0.3819, Train Accuracy: 89.7860, Test Accuracy: 88.0100\n",
            "Epoch: 17, Train Loss: 0.2620, Test Loss: 0.3977, Train Accuracy: 90.6560, Test Accuracy: 87.3000\n",
            "Epoch: 18, Train Loss: 0.2316, Test Loss: 0.3778, Train Accuracy: 91.8300, Test Accuracy: 88.5500\n",
            "Epoch: 19, Train Loss: 0.2075, Test Loss: 0.3574, Train Accuracy: 92.6560, Test Accuracy: 88.6700\n",
            "Epoch: 20, Train Loss: 0.1846, Test Loss: 0.3182, Train Accuracy: 93.4520, Test Accuracy: 89.8900\n",
            "Epoch: 21, Train Loss: 0.1618, Test Loss: 0.3335, Train Accuracy: 94.3920, Test Accuracy: 89.9300\n",
            "Epoch: 22, Train Loss: 0.1375, Test Loss: 0.3215, Train Accuracy: 95.2040, Test Accuracy: 90.3400\n",
            "Epoch: 23, Train Loss: 0.1192, Test Loss: 0.3377, Train Accuracy: 95.9220, Test Accuracy: 89.8200\n",
            "Epoch: 24, Train Loss: 0.0991, Test Loss: 0.3011, Train Accuracy: 96.7380, Test Accuracy: 90.8800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plot_misclassified_images(data=misclassified_image_data, class_label=classes, num_images=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qcAyZDnaBkHm",
        "outputId": "e4540ac3-fd4a-4d19-90fe-dbd345708e1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plot_gradcam_images(\n",
        "    model=model,\n",
        "    data=misclassified_image_data,\n",
        "    class_label=classes,\n",
        "    # Use penultimate block of resnet18 layer 3 as the target layer for gradcam\n",
        "    # Decided using model summary so that dimensions > 7x7\n",
        "    target_layers=[model.layer3_r2[-1]],\n",
        "    targets=None,\n",
        "    num_images=10,\n",
        "    image_weight=0.6,\n",
        ")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "8NV8-WepBkuv",
        "outputId": "d5f281ee-6cce-4bdb-aaad-b7f0e776640f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext tensorboard\n",
        "\n",
        "!tensorboard --logdir {logging_path}"
      ],
      "metadata": {
        "id": "hWAQ0dCLG9of",
        "outputId": "df2deeaa-19dc-4abf-8ce2-fab31d065f60",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-04-25 07:15:25.031750: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-04-25 07:15:25.031804: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-04-25 07:15:25.033155: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-04-25 07:15:26.005767: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\n",
            "NOTE: Using experimental fast data loading logic. To disable, pass\n",
            "    \"--load_fast=false\" and report issues on GitHub. More details:\n",
            "    https://github.com/tensorflow/tensorboard/issues/4784\n",
            "\n",
            "Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
            "TensorBoard 2.15.2 at http://localhost:6006/ (Press CTRL+C to quit)\n"
          ]
        }
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "widgets": {
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            "_model_module_version": "1.5.0",
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            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
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            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
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            "description_tooltip": null,
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