ViT_Training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ViT-Training.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "ZTi5gkvLTPy_",
        "YoNwBENCZFJz",
        "mzZLdR1XZKDN",
        "W05QsNpdZUxE",
        "niiwVIssZjXy",
        "01dU14-hZs4B",
        "ALbURc0wZ-0v",
        "1Q1rZSxzk3MV"
      ],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chandlerbing65nm/Cassava-Leaf-Disease-Classification/blob/main/ViT_Training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTi5gkvLTPy_"
      },
      "source": [
        "# Check Resources"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqE4aRpYTLqa",
        "outputId": "8b7ffd7d-e9fe-4ed7-be53-3b6e98f70c42"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Sep 26 08:13:36 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.63.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  A100-SXM4-40GB      Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P0    43W / 400W |      0MiB / 40536MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEyodftlTTLW",
        "outputId": "2b139ec7-40c3-4e99-d4b5-a023d5b829f4"
      },
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n",
        "  print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n",
        "  print('re-execute this cell.')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 89.6 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2T-WxqFmUjKG"
      },
      "source": [
        "# Mount Your GDrive Here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yWlcISLUkHe",
        "outputId": "e7d478bf-47a1-4ffa-f740-26f770563277"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\", force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cx0UNpPpUlX1",
        "outputId": "8f04bde4-6710-43d7-f5ca-c4e4a84bfb07"
      },
      "source": [
        "!cp -r /content/drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease /content\n",
        "%cd Cassava-Leaf-Disease"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Cassava-Leaf-Disease\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXzs-_IhTU0g"
      },
      "source": [
        "# Install Packages and Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxEY3yTGTa4h",
        "outputId": "7b061f75-a224-4c8a-a467-8e14c0b3bbe3"
      },
      "source": [
        "!pip install rdkit-pypi\n",
        "!pip install ipykernel\n",
        "!pip install pydicom\n",
        "!pip install catalyst"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rdkit-pypi\n",
            "  Downloading rdkit_pypi-2021.3.5.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (19.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 19.7 MB 184 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.7/dist-packages (from rdkit-pypi) (1.19.5)\n",
            "Installing collected packages: rdkit-pypi\n",
            "Successfully installed rdkit-pypi-2021.3.5.1\n",
            "Requirement already satisfied: ipykernel in /usr/local/lib/python3.7/dist-packages (4.10.1)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel) (5.1.1)\n",
            "Requirement already satisfied: traitlets>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel) (5.1.0)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel) (5.3.5)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel) (5.5.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (4.4.2)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (4.8.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (57.4.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (1.0.18)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (0.8.1)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (2.6.1)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipykernel) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipykernel) (0.2.5)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel) (22.3.0)\n",
            "Requirement already satisfied: jupyter-core>=4.6.0 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel) (4.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel) (2.8.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython>=4.0.0->ipykernel) (0.7.0)\n",
            "Collecting pydicom\n",
            "  Downloading pydicom-2.2.1-py3-none-any.whl (2.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: pydicom\n",
            "Successfully installed pydicom-2.2.1\n",
            "Collecting catalyst\n",
            "  Downloading catalyst-21.8-py2.py3-none-any.whl (556 kB)\n",
            "\u001b[K     |████████████████████████████████| 556 kB 5.1 MB/s \n",
            "\u001b[?25hCollecting PyYAML>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 45.6 MB/s \n",
            "\u001b[?25hCollecting hydra-slayer>=0.1.1\n",
            "  Downloading hydra_slayer-0.2.0-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: torch>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from catalyst) (1.9.0+cu102)\n",
            "Requirement already satisfied: numpy>=1.18 in /usr/local/lib/python3.7/dist-packages (from catalyst) (1.19.5)\n",
            "Requirement already satisfied: tqdm>=4.33.0 in /usr/local/lib/python3.7/dist-packages (from catalyst) (4.62.2)\n",
            "Collecting tensorboardX<2.3.0>=2.1.0\n",
            "  Downloading tensorboardX-2.2-py2.py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 69.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX<2.3.0>=2.1.0->catalyst) (3.17.3)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.8.0->tensorboardX<2.3.0>=2.1.0->catalyst) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.3.0->catalyst) (3.7.4.3)\n",
            "Installing collected packages: tensorboardX, PyYAML, hydra-slayer, catalyst\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-5.4.1 catalyst-21.8 hydra-slayer-0.2.0 tensorboardX-2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KA4gHFffafKW",
        "outputId": "df87937b-5159-44ed-a0ba-50beb25652a7"
      },
      "source": [
        "!wget -q https://github.com/albumentations-team/albumentations_examples/archive/master.zip -O /tmp/albumentations_examples.zip\n",
        "!unzip -o -qq /tmp/albumentations_examples.zip -d /tmp/albumentations_examples\n",
        "!cp -r /tmp/albumentations_examples/albumentations_examples-master/notebooks/images .\n",
        "!echo \"Images are successfully downloaded\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images are successfully downloaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1AJm49pcYWP",
        "outputId": "18eeac00-94ab-4071-edc3-59e3c5f2a584"
      },
      "source": [
        "!pip install -q -U albumentations\n",
        "!echo \"$(pip freeze | grep albumentations) is successfully installed\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 98 kB 3.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 37.1 MB 98 kB/s \n",
            "\u001b[?25halbumentations==1.0.3 is successfully installed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oiAmaFxPRGQX",
        "outputId": "1480dafa-db26-45de-d778-5114211025f0"
      },
      "source": [
        "!pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torch==1.8.0+cu111\n",
            "  Downloading https://download.pytorch.org/whl/cu111/torch-1.8.0%2Bcu111-cp37-cp37m-linux_x86_64.whl (1982.2 MB)\n",
            "\u001b[K     |█████████████▌                  | 834.1 MB 1.2 MB/s eta 0:15:43tcmalloc: large alloc 1147494400 bytes == 0x563ae018e000 @  0x7f80dce41615 0x563adcf4c4cc 0x563add02c47a 0x563adcf4f2ed 0x563add040e1d 0x563adcfc2e99 0x563adcfbd9ee 0x563adcf50bda 0x563adcfc2d00 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563adcf51039 0x563adcf94409 0x563adcf4fc52 0x563adcfc2c25 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbe915 0x563adcf50afa 0x563adcfbec0d 0x563adcfbd9ee\n",
            "\u001b[K     |█████████████████               | 1055.7 MB 1.2 MB/s eta 0:13:12tcmalloc: large alloc 1434370048 bytes == 0x563b247e4000 @  0x7f80dce41615 0x563adcf4c4cc 0x563add02c47a 0x563adcf4f2ed 0x563add040e1d 0x563adcfc2e99 0x563adcfbd9ee 0x563adcf50bda 0x563adcfc2d00 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563adcf51039 0x563adcf94409 0x563adcf4fc52 0x563adcfc2c25 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbe915 0x563adcf50afa 0x563adcfbec0d 0x563adcfbd9ee\n",
            "\u001b[K     |█████████████████████▋          | 1336.2 MB 1.2 MB/s eta 0:09:07tcmalloc: large alloc 1792966656 bytes == 0x563b79fd0000 @  0x7f80dce41615 0x563adcf4c4cc 0x563add02c47a 0x563adcf4f2ed 0x563add040e1d 0x563adcfc2e99 0x563adcfbd9ee 0x563adcf50bda 0x563adcfc2d00 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563adcf51039 0x563adcf94409 0x563adcf4fc52 0x563adcfc2c25 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbe915 0x563adcf50afa 0x563adcfbec0d 0x563adcfbd9ee\n",
            "\u001b[K     |███████████████████████████▎    | 1691.1 MB 1.2 MB/s eta 0:04:05tcmalloc: large alloc 2241208320 bytes == 0x563ae018e000 @  0x7f80dce41615 0x563adcf4c4cc 0x563add02c47a 0x563adcf4f2ed 0x563add040e1d 0x563adcfc2e99 0x563adcfbd9ee 0x563adcf50bda 0x563adcfc2d00 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563add041c66 0x563adcfbedaf 0x563adcf51039 0x563adcf94409 0x563adcf4fc52 0x563adcfc2c25 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbe915 0x563adcf50afa 0x563adcfbec0d 0x563adcfbd9ee\n",
            "\u001b[K     |████████████████████████████████| 1982.2 MB 1.1 MB/s eta 0:00:01tcmalloc: large alloc 1982251008 bytes == 0x563b65af0000 @  0x7f80dce401e7 0x563adcf82067 0x563adcf4c4cc 0x563add02c47a 0x563adcf4f2ed 0x563add040e1d 0x563adcfc2e99 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcf50afa 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee\n",
            "tcmalloc: large alloc 2477817856 bytes == 0x563bdbd5c000 @  0x7f80dce41615 0x563adcf4c4cc 0x563add02c47a 0x563adcf4f2ed 0x563add040e1d 0x563adcfc2e99 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbec0d 0x563adcf50afa 0x563adcfbec0d 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee 0x563adcf50bda 0x563adcfbf737 0x563adcfbd9ee 0x563adcf51271\n",
            "\u001b[K     |████████████████████████████████| 1982.2 MB 2.9 kB/s \n",
            "\u001b[?25hCollecting torchvision==0.9.0+cu111\n",
            "  Downloading https://download.pytorch.org/whl/cu111/torchvision-0.9.0%2Bcu111-cp37-cp37m-linux_x86_64.whl (17.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 17.6 MB 12 kB/s \n",
            "\u001b[?25hCollecting torchaudio==0.8.0\n",
            "  Downloading torchaudio-0.8.0-cp37-cp37m-manylinux1_x86_64.whl (1.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.8.0+cu111) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.0+cu111) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.0+cu111) (7.1.2)\n",
            "Installing collected packages: torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.9.0+cu102\n",
            "    Uninstalling torch-1.9.0+cu102:\n",
            "      Successfully uninstalled torch-1.9.0+cu102\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.10.0+cu102\n",
            "    Uninstalling torchvision-0.10.0+cu102:\n",
            "      Successfully uninstalled torchvision-0.10.0+cu102\n",
            "\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",
            "torchtext 0.10.0 requires torch==1.9.0, but you have torch 1.8.0+cu111 which is incompatible.\u001b[0m\n",
            "Successfully installed torch-1.8.0+cu111 torchaudio-0.8.0 torchvision-0.9.0+cu111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6E_qVsbKVU9Q"
      },
      "source": [
        "#Training and Data Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YoNwBENCZFJz"
      },
      "source": [
        "## Pre-setting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6DkPhsLVYOP"
      },
      "source": [
        "package_paths = [\n",
        "    'input/pytorch-image-models/pytorch-image-models-master', 'input/image-fmix/FMix-master'\n",
        "]\n",
        "import sys; \n",
        "\n",
        "for pth in package_paths:\n",
        "    sys.path.append(pth)\n",
        "from fmix import sample_mask, make_low_freq_image, binarise_mask"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3OuZ_8PVgF_"
      },
      "source": [
        "from glob import glob\n",
        "from sklearn.model_selection import GroupKFold, StratifiedKFold\n",
        "import cv2\n",
        "from skimage import io\n",
        "import torch\n",
        "from torch import nn\n",
        "import os\n",
        "from datetime import datetime\n",
        "import time\n",
        "import random\n",
        "import cv2\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import Dataset,DataLoader\n",
        "from torch.utils.data.sampler import SequentialSampler, RandomSampler\n",
        "from torch.cuda.amp import autocast, GradScaler\n",
        "from torch.nn.modules.loss import _WeightedLoss\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import timm\n",
        "\n",
        "import catalyst\n",
        "import sklearn\n",
        "import warnings\n",
        "import joblib\n",
        "from sklearn.metrics import roc_auc_score, log_loss\n",
        "from sklearn import metrics\n",
        "import warnings\n",
        "import cv2\n",
        "import pydicom\n",
        "#from efficientnet_pytorch import EfficientNet\n",
        "from scipy.ndimage.interpolation import zoom\n",
        "\n",
        "from albumentations import (\n",
        "    HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,\n",
        "    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,\n",
        "    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,\n",
        "    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose, Normalize, Cutout, CoarseDropout, ShiftScaleRotate, CenterCrop, Resize\n",
        ")\n",
        "from albumentations.pytorch import ToTensorV2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaWVYqpjVngY"
      },
      "source": [
        "CFG = {\n",
        "    'fold_num': 5,\n",
        "    'seed': 719,\n",
        "    'model_arch': 'vit_base_patch16_224',\n",
        "    'img_size': 224,\n",
        "    'epochs': 10,\n",
        "    'train_bs': 16,\n",
        "    'valid_bs': 32,\n",
        "    'T_0': 10,\n",
        "    'lr': 1e-4,\n",
        "    'min_lr': 1e-6,\n",
        "    'weight_decay':1e-6,\n",
        "    'num_workers': 4,\n",
        "    'accum_iter': 2, # suppoprt to do batch accumulation for backprop with effectively larger batch size\n",
        "    'verbose_step': 1,\n",
        "    'device': 'cuda:0'\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "6vHnjndyY3Rj",
        "outputId": "17a3b68d-0e9a-4add-e7bc-dfde3674cef5"
      },
      "source": [
        "train = pd.read_csv('input/cassava-leaf-disease-classification/train.csv')\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000015157.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000201771.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100042118.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000723321.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000812911.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         image_id  label\n",
              "0  1000015157.jpg      0\n",
              "1  1000201771.jpg      3\n",
              "2   100042118.jpg      1\n",
              "3  1000723321.jpg      1\n",
              "4  1000812911.jpg      3"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ypQcAbf1Y7fM",
        "outputId": "e7a9cf52-5bca-44d6-d4f2-596bc16297f7"
      },
      "source": [
        "train.label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    13158\n",
              "4     2577\n",
              "2     2386\n",
              "1     2189\n",
              "0     1087\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "dBzFk7OiY_HR",
        "outputId": "29df8aab-71d7-4239-ba1e-74a934ad1a52"
      },
      "source": [
        "submission = pd.read_csv('input/cassava-leaf-disease-classification/sample_submission.csv')\n",
        "submission.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2216849948.jpg</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         image_id  label\n",
              "0  2216849948.jpg      4"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzZLdR1XZKDN"
      },
      "source": [
        "## Helper Functions (perform this twice)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97X_lKKKZMp-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "79fc3adc-d2a1-4eb9-a904-87ac009994b9"
      },
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "    \n",
        "def get_img(pathw):\n",
        "    im_bgr = cv2.imread(pathw)\n",
        "    im_rgb = im_bgr[:, :, ::-1]\n",
        "    return im_rgb\n",
        "\n",
        "img = get_img('input/cassava-leaf-disease-classification/train_images/1000015157.jpg')\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W05QsNpdZUxE"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3DeWc-NZWRm"
      },
      "source": [
        "def rand_bbox(size, lam):\n",
        "    W = size[0]\n",
        "    H = size[1]\n",
        "    cut_rat = np.sqrt(1. - lam)\n",
        "    cut_w = np.int(W * cut_rat)\n",
        "    cut_h = np.int(H * cut_rat)\n",
        "\n",
        "    # uniform\n",
        "    cx = np.random.randint(W)\n",
        "    cy = np.random.randint(H)\n",
        "\n",
        "    bbx1 = np.clip(cx - cut_w // 2, 0, W)\n",
        "    bby1 = np.clip(cy - cut_h // 2, 0, H)\n",
        "    bbx2 = np.clip(cx + cut_w // 2, 0, W)\n",
        "    bby2 = np.clip(cy + cut_h // 2, 0, H)\n",
        "    return bbx1, bby1, bbx2, bby2\n",
        "\n",
        "\n",
        "class CassavaDataset(Dataset):\n",
        "    def __init__(self, df, data_root, \n",
        "                 transforms=None, \n",
        "                 output_label=True, \n",
        "                 one_hot_label=False,\n",
        "                 do_fmix=False, \n",
        "                 fmix_params={\n",
        "                     'alpha': 1., \n",
        "                     'decay_power': 3., \n",
        "                     'shape': (CFG['img_size'], CFG['img_size']),\n",
        "                     'max_soft': True, \n",
        "                     'reformulate': False\n",
        "                 },\n",
        "                 do_cutmix=False,\n",
        "                 cutmix_params={\n",
        "                     'alpha': 1,\n",
        "                 }\n",
        "                ):\n",
        "        \n",
        "        super().__init__()\n",
        "        self.df = df.reset_index(drop=True).copy()\n",
        "        self.transforms = transforms\n",
        "        self.data_root = data_root\n",
        "        self.do_fmix = do_fmix\n",
        "        self.fmix_params = fmix_params\n",
        "        self.do_cutmix = do_cutmix\n",
        "        self.cutmix_params = cutmix_params\n",
        "        \n",
        "        self.output_label = output_label\n",
        "        self.one_hot_label = one_hot_label\n",
        "        \n",
        "        if output_label == True:\n",
        "            self.labels = self.df['label'].values\n",
        "            #print(self.labels)\n",
        "            \n",
        "            if one_hot_label is True:\n",
        "                self.labels = np.eye(self.df['label'].max()+1)[self.labels]\n",
        "                #print(self.labels)\n",
        "            \n",
        "    def __len__(self):\n",
        "        return self.df.shape[0]\n",
        "    \n",
        "    def __getitem__(self, index: int):\n",
        "        \n",
        "        # get labels\n",
        "        if self.output_label:\n",
        "            target = self.labels[index]\n",
        "          \n",
        "        img  = get_img(\"{}/{}\".format(self.data_root, self.df.loc[index]['image_id']))\n",
        "\n",
        "        if self.transforms:\n",
        "            img = self.transforms(image=img)['image']\n",
        "        \n",
        "        if self.do_fmix and np.random.uniform(0., 1., size=1)[0] > 0.5:\n",
        "            with torch.no_grad():\n",
        "                #lam, mask = sample_mask(**self.fmix_params)\n",
        "                \n",
        "                lam = np.clip(np.random.beta(self.fmix_params['alpha'], self.fmix_params['alpha']),0.6,0.7)\n",
        "                \n",
        "                # Make mask, get mean / std\n",
        "                mask = make_low_freq_image(self.fmix_params['decay_power'], self.fmix_params['shape'])\n",
        "                mask = binarise_mask(mask, lam, self.fmix_params['shape'], self.fmix_params['max_soft'])\n",
        "    \n",
        "                fmix_ix = np.random.choice(self.df.index, size=1)[0]\n",
        "                fmix_img  = get_img(\"{}/{}\".format(self.data_root, self.df.iloc[fmix_ix]['image_id']))\n",
        "\n",
        "                if self.transforms:\n",
        "                    fmix_img = self.transforms(image=fmix_img)['image']\n",
        "\n",
        "                mask_torch = torch.from_numpy(mask)\n",
        "                \n",
        "                # mix image\n",
        "                img = mask_torch*img+(1.-mask_torch)*fmix_img\n",
        "\n",
        "                #print(mask.shape)\n",
        "\n",
        "                #assert self.output_label==True and self.one_hot_label==True\n",
        "\n",
        "                # mix target\n",
        "                rate = mask.sum()/CFG['img_size']/CFG['img_size']\n",
        "                target = rate*target + (1.-rate)*self.labels[fmix_ix]\n",
        "                #print(target, mask, img)\n",
        "                #assert False\n",
        "        \n",
        "        if self.do_cutmix and np.random.uniform(0., 1., size=1)[0] > 0.5:\n",
        "            #print(img.sum(), img.shape)\n",
        "            with torch.no_grad():\n",
        "                cmix_ix = np.random.choice(self.df.index, size=1)[0]\n",
        "                cmix_img  = get_img(\"{}/{}\".format(self.data_root, self.df.iloc[cmix_ix]['image_id']))\n",
        "                if self.transforms:\n",
        "                    cmix_img = self.transforms(image=cmix_img)['image']\n",
        "                    \n",
        "                lam = np.clip(np.random.beta(self.cutmix_params['alpha'], self.cutmix_params['alpha']),0.3,0.4)\n",
        "                bbx1, bby1, bbx2, bby2 = rand_bbox((CFG['img_size'], CFG['img_size']), lam)\n",
        "\n",
        "                img[:, bbx1:bbx2, bby1:bby2] = cmix_img[:, bbx1:bbx2, bby1:bby2]\n",
        "\n",
        "                rate = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (CFG['img_size'] * CFG['img_size']))\n",
        "                target = rate*target + (1.-rate)*self.labels[cmix_ix]\n",
        "                \n",
        "            #print('-', img.sum())\n",
        "            #print(target)\n",
        "            #assert False\n",
        "                            \n",
        "        # do label smoothing\n",
        "        #print(type(img), type(target))\n",
        "        if self.output_label == True:\n",
        "            return img, target\n",
        "        else:\n",
        "            return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niiwVIssZjXy"
      },
      "source": [
        "## Define Training and Validation Image Augmentations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCkDCkP8Znt8"
      },
      "source": [
        "def get_train_transforms():\n",
        "    return Compose([\n",
        "            RandomResizedCrop(CFG['img_size'], CFG['img_size']),\n",
        "            Transpose(p=0.5),\n",
        "            HorizontalFlip(p=0.5),\n",
        "            VerticalFlip(p=0.5),\n",
        "            ShiftScaleRotate(p=0.5),\n",
        "            HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5),\n",
        "            RandomBrightnessContrast(brightness_limit=(-0.1,0.1), contrast_limit=(-0.1, 0.1), p=0.5),\n",
        "            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\n",
        "            CoarseDropout(p=0.5),\n",
        "            Cutout(p=0.5),\n",
        "            ToTensorV2(p=1.0),\n",
        "        ], p=1.)\n",
        "  \n",
        "        \n",
        "def get_valid_transforms():\n",
        "    return Compose([\n",
        "            CenterCrop(CFG['img_size'], CFG['img_size'], p=1.),\n",
        "            Resize(CFG['img_size'], CFG['img_size']),\n",
        "            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\n",
        "            ToTensorV2(p=1.0),\n",
        "        ], p=1.)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01dU14-hZs4B"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8whBqY-ZuJr"
      },
      "source": [
        "class CassvaImgClassifier(nn.Module):\n",
        "    def __init__(self, model_arch, n_class, pretrained=False):\n",
        "        super().__init__()\n",
        "        self.model = timm.create_model(model_arch, pretrained=pretrained)\n",
        "        #n_features = self.model.classifier.in_features\n",
        "        #self.model.classifier = nn.Linear(n_features, n_class)\n",
        "        n_features = self.model.head.in_features\n",
        "        self.model.head = nn.Linear(n_features, n_class)\n",
        "        '''\n",
        "        self.model.classifier = nn.Sequential(\n",
        "            nn.Dropout(0.3),\n",
        "            #nn.Linear(n_features, hidden_size,bias=True), nn.ELU(),\n",
        "            nn.Linear(n_features, n_class, bias=True)\n",
        "        )\n",
        "        '''\n",
        "    def forward(self, x):\n",
        "        x = self.model(x)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALbURc0wZ-0v"
      },
      "source": [
        "## Training APIs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aH6jlm36Z__i"
      },
      "source": [
        "def prepare_dataloader(df, trn_idx, val_idx, data_root='input/cassava-leaf-disease-classification/train_images/'):\n",
        "    \n",
        "    from catalyst.data.sampler import BalanceClassSampler\n",
        "    \n",
        "    train_ = df.loc[trn_idx,:].reset_index(drop=True)\n",
        "    valid_ = df.loc[val_idx,:].reset_index(drop=True)\n",
        "        \n",
        "    train_ds = CassavaDataset(train_, data_root, transforms=get_train_transforms(), output_label=True, one_hot_label=False, do_fmix=False, do_cutmix=False)\n",
        "    valid_ds = CassavaDataset(valid_, data_root, transforms=get_valid_transforms(), output_label=True)\n",
        "    \n",
        "    train_loader = torch.utils.data.DataLoader(\n",
        "        train_ds,\n",
        "        batch_size=CFG['train_bs'],\n",
        "        pin_memory=False,\n",
        "        drop_last=False,\n",
        "        shuffle=True,        \n",
        "        num_workers=CFG['num_workers'],\n",
        "        #sampler=BalanceClassSampler(labels=train_['label'].values, mode=\"downsampling\")\n",
        "    )\n",
        "    val_loader = torch.utils.data.DataLoader(\n",
        "        valid_ds, \n",
        "        batch_size=CFG['valid_bs'],\n",
        "        num_workers=CFG['num_workers'],\n",
        "        shuffle=False,\n",
        "        pin_memory=False,\n",
        "    )\n",
        "    return train_loader, val_loader\n",
        "\n",
        "def train_one_epoch(epoch, model, loss_fn, optimizer, train_loader, device, scheduler=None, schd_batch_update=False):\n",
        "    model.train()\n",
        "\n",
        "    t = time.time()\n",
        "    running_loss = None\n",
        "\n",
        "    pbar = tqdm(enumerate(train_loader), total=len(train_loader))\n",
        "    for step, (imgs, image_labels) in pbar:\n",
        "        imgs = imgs.to(device).float()\n",
        "        image_labels = image_labels.to(device).long()\n",
        "\n",
        "        #print(image_labels.shape, exam_label.shape)\n",
        "        with autocast():\n",
        "            image_preds = model(imgs)   #output = model(input)\n",
        "            #print(image_preds.shape, exam_pred.shape)\n",
        "\n",
        "            loss = loss_fn(image_preds, image_labels)\n",
        "            \n",
        "            scaler.scale(loss).backward()\n",
        "\n",
        "            if running_loss is None:\n",
        "                running_loss = loss.item()\n",
        "            else:\n",
        "                running_loss = running_loss * .99 + loss.item() * .01\n",
        "\n",
        "            if ((step + 1) %  CFG['accum_iter'] == 0) or ((step + 1) == len(train_loader)):\n",
        "                # may unscale_ here if desired (e.g., to allow clipping unscaled gradients)\n",
        "\n",
        "                scaler.step(optimizer)\n",
        "                scaler.update()\n",
        "                optimizer.zero_grad() \n",
        "                \n",
        "                if scheduler is not None and schd_batch_update:\n",
        "                    scheduler.step()\n",
        "\n",
        "            if ((step + 1) % CFG['verbose_step'] == 0) or ((step + 1) == len(train_loader)):\n",
        "                description = f'epoch {epoch} loss: {running_loss:.4f}'\n",
        "                \n",
        "                pbar.set_description(description)\n",
        "                \n",
        "    if scheduler is not None and not schd_batch_update:\n",
        "        scheduler.step()\n",
        "        \n",
        "def valid_one_epoch(epoch, model, loss_fn, val_loader, device, scheduler=None, schd_loss_update=False):\n",
        "    model.eval()\n",
        "\n",
        "    t = time.time()\n",
        "    loss_sum = 0\n",
        "    sample_num = 0\n",
        "    image_preds_all = []\n",
        "    image_targets_all = []\n",
        "    \n",
        "    pbar = tqdm(enumerate(val_loader), total=len(val_loader))\n",
        "    for step, (imgs, image_labels) in pbar:\n",
        "        imgs = imgs.to(device).float()\n",
        "        image_labels = image_labels.to(device).long()\n",
        "        \n",
        "        image_preds = model(imgs)   #output = model(input)\n",
        "        #print(image_preds.shape, exam_pred.shape)\n",
        "        image_preds_all += [torch.argmax(image_preds, 1).detach().cpu().numpy()]\n",
        "        image_targets_all += [image_labels.detach().cpu().numpy()]\n",
        "        \n",
        "        loss = loss_fn(image_preds, image_labels)\n",
        "        \n",
        "        loss_sum += loss.item()*image_labels.shape[0]\n",
        "        sample_num += image_labels.shape[0]  \n",
        "\n",
        "        if ((step + 1) % CFG['verbose_step'] == 0) or ((step + 1) == len(val_loader)):\n",
        "            description = f'epoch {epoch} loss: {loss_sum/sample_num:.4f}'\n",
        "            pbar.set_description(description)\n",
        "    \n",
        "    image_preds_all = np.concatenate(image_preds_all)\n",
        "    image_targets_all = np.concatenate(image_targets_all)\n",
        "    print('validation multi-class accuracy = {:.4f}'.format((image_preds_all==image_targets_all).mean()))\n",
        "    \n",
        "    if scheduler is not None:\n",
        "        if schd_loss_update:\n",
        "            scheduler.step(loss_sum/sample_num)\n",
        "        else:\n",
        "            scheduler.step()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuiHdOx7k0oX"
      },
      "source": [
        "# reference: https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/173733\n",
        "class MyCrossEntropyLoss(_WeightedLoss):\n",
        "    def __init__(self, weight=None, reduction='mean'):\n",
        "        super().__init__(weight=weight, reduction=reduction)\n",
        "        self.weight = weight\n",
        "        self.reduction = reduction\n",
        "\n",
        "    def forward(self, inputs, targets):\n",
        "        lsm = F.log_softmax(inputs, -1)\n",
        "\n",
        "        if self.weight is not None:\n",
        "            lsm = lsm * self.weight.unsqueeze(0)\n",
        "\n",
        "        loss = -(targets * lsm).sum(-1)\n",
        "\n",
        "        if  self.reduction == 'sum':\n",
        "            loss = loss.sum()\n",
        "        elif  self.reduction == 'mean':\n",
        "            loss = loss.mean()\n",
        "\n",
        "        return loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Q1rZSxzk3MV"
      },
      "source": [
        "## Main Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IS7ye8tk5DW",
        "outputId": "125e3212-efb6-4d6c-b3f0-6ce636f6516e"
      },
      "source": [
        "if __name__ == '__main__':\n",
        "     # for training only, need nightly build pytorch\n",
        "\n",
        "    seed_everything(CFG['seed'])\n",
        "    \n",
        "    folds = StratifiedKFold(n_splits=CFG['fold_num'], shuffle=True, random_state=CFG['seed']).split(np.arange(train.shape[0]), train.label.values)\n",
        "    \n",
        "    for fold, (trn_idx, val_idx) in enumerate(folds):\n",
        "        print('\\nTraining with fold {} started'.format(fold))\n",
        "\n",
        "        print(len(trn_idx), len(val_idx))\n",
        "        train_loader, val_loader = prepare_dataloader(train, trn_idx, val_idx, data_root='input/cassava-leaf-disease-classification/train_images/')\n",
        "\n",
        "        device = torch.device(CFG['device'])\n",
        "        \n",
        "        model = CassvaImgClassifier(CFG['model_arch'], train.label.nunique(), pretrained=True).to(device)\n",
        "        scaler = GradScaler()   \n",
        "        optimizer = torch.optim.Adam(model.parameters(), lr=CFG['lr'], weight_decay=CFG['weight_decay'])\n",
        "        #scheduler = torch.optim.lr_scheduler.StepLR(optimizer, gamma=0.1, step_size=CFG['epochs']-1)\n",
        "        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=CFG['T_0'], T_mult=1, eta_min=CFG['min_lr'], last_epoch=-1)\n",
        "        #scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, pct_start=0.1, div_factor=25, \n",
        "        #                                                max_lr=CFG['lr'], epochs=CFG['epochs'], steps_per_epoch=len(train_loader))\n",
        "        \n",
        "        loss_tr = nn.CrossEntropyLoss().to(device) #MyCrossEntropyLoss().to(device)\n",
        "        loss_fn = nn.CrossEntropyLoss().to(device)\n",
        "        \n",
        "        for epoch in range(CFG['epochs']):\n",
        "            train_one_epoch(epoch, model, loss_tr, optimizer, train_loader, device, scheduler=scheduler, schd_batch_update=False)\n",
        "\n",
        "            with torch.no_grad():\n",
        "                valid_one_epoch(epoch, model, loss_fn, val_loader, device, scheduler=None, schd_loss_update=False)\n",
        "\n",
        "            torch.save(model.state_dict(),'output/{}_fold_{}_{}'.format(CFG['model_arch'], fold, epoch))\n",
        "            \n",
        "        #torch.save(model.cnn_model.state_dict(),'{}/cnn_model_fold_{}_{}'.format(CFG['model_path'], fold, CFG['tag']))\n",
        "        del model, optimizer, train_loader, val_loader, scaler, scheduler\n",
        "        torch.cuda.empty_cache()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training with 0 started\n",
            "17117 4280\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/transforms.py:690: FutureWarning: This class has been deprecated. Please use CoarseDropout\n",
            "  FutureWarning,\n",
            "Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth\" to /root/.cache/torch/hub/checkpoints/jx_vit_base_p16_224-80ecf9dd.pth\n",
            "epoch 0 loss: 0.5482: 100%|██████████| 1070/1070 [39:07<00:00,  2.19s/it]\n",
            "epoch 0 loss: 0.4632: 100%|██████████| 134/134 [18:58<00:00,  8.49s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8395\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 1 loss: 0.4518: 100%|██████████| 1070/1070 [01:12<00:00, 14.81it/s]\n",
            "epoch 1 loss: 0.4598: 100%|██████████| 134/134 [00:12<00:00, 10.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8416\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 2 loss: 0.4454: 100%|██████████| 1070/1070 [01:12<00:00, 14.80it/s]\n",
            "epoch 2 loss: 0.4733: 100%|██████████| 134/134 [00:12<00:00, 10.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8339\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 3 loss: 0.3861: 100%|██████████| 1070/1070 [01:12<00:00, 14.82it/s]\n",
            "epoch 3 loss: 0.4320: 100%|██████████| 134/134 [00:12<00:00, 10.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8488\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 4 loss: 0.3901: 100%|██████████| 1070/1070 [01:12<00:00, 14.81it/s]\n",
            "epoch 4 loss: 0.4827: 100%|██████████| 134/134 [00:12<00:00, 10.59it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8325\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 5 loss: 0.3483: 100%|██████████| 1070/1070 [01:12<00:00, 14.81it/s]\n",
            "epoch 5 loss: 0.4203: 100%|██████████| 134/134 [00:12<00:00, 10.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8563\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 6 loss: 0.3050: 100%|██████████| 1070/1070 [01:12<00:00, 14.80it/s]\n",
            "epoch 6 loss: 0.4085: 100%|██████████| 134/134 [00:12<00:00, 10.60it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8633\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 7 loss: 0.2887: 100%|██████████| 1070/1070 [01:12<00:00, 14.80it/s]\n",
            "epoch 7 loss: 0.3995: 100%|██████████| 134/134 [00:12<00:00, 10.55it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8631\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 8 loss: 0.2499: 100%|██████████| 1070/1070 [01:12<00:00, 14.78it/s]\n",
            "epoch 8 loss: 0.4036: 100%|██████████| 134/134 [00:12<00:00, 10.51it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8682\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 9 loss: 0.2304: 100%|██████████| 1070/1070 [01:12<00:00, 14.78it/s]\n",
            "epoch 9 loss: 0.4011: 100%|██████████| 134/134 [00:12<00:00, 10.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8680\n",
            "Training with 1 started\n",
            "17117 4280\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 0 loss: 0.4635: 100%|██████████| 1070/1070 [01:12<00:00, 14.69it/s]\n",
            "epoch 0 loss: 0.5591: 100%|██████████| 134/134 [00:12<00:00, 10.73it/s]\n"
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            "validation multi-class accuracy = 0.8042\n"
          ]
        },
        {
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          "text": [
            "epoch 1 loss: 0.4308: 100%|██████████| 1070/1070 [01:12<00:00, 14.74it/s]\n",
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            "validation multi-class accuracy = 0.8297\n"
          ]
        },
        {
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          "text": [
            "epoch 2 loss: 0.4105: 100%|██████████| 1070/1070 [01:12<00:00, 14.69it/s]\n",
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            "validation multi-class accuracy = 0.8278\n"
          ]
        },
        {
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          "text": [
            "epoch 3 loss: 0.3883: 100%|██████████| 1070/1070 [01:12<00:00, 14.78it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8495\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 4 loss: 0.3839: 100%|██████████| 1070/1070 [01:12<00:00, 14.75it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8411\n"
          ]
        },
        {
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          "text": [
            "epoch 5 loss: 0.3193: 100%|██████████| 1070/1070 [01:12<00:00, 14.74it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8456\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 6 loss: 0.3187: 100%|██████████| 1070/1070 [01:12<00:00, 14.76it/s]\n",
            "epoch 6 loss: 0.4127: 100%|██████████| 134/134 [00:12<00:00, 10.75it/s]\n"
          ]
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          "text": [
            "validation multi-class accuracy = 0.8645\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 7 loss: 0.2795: 100%|██████████| 1070/1070 [01:12<00:00, 14.76it/s]\n",
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          ]
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          "text": [
            "validation multi-class accuracy = 0.8629\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 8 loss: 0.2536: 100%|██████████| 1070/1070 [01:12<00:00, 14.73it/s]\n",
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          ]
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          "text": [
            "validation multi-class accuracy = 0.8624\n"
          ]
        },
        {
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          "text": [
            "epoch 9 loss: 0.2399: 100%|██████████| 1070/1070 [01:12<00:00, 14.77it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8617\n",
            "Training with 2 started\n",
            "17118 4279\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 0 loss: 0.4569: 100%|██████████| 1070/1070 [01:12<00:00, 14.66it/s]\n",
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          ]
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            "validation multi-class accuracy = 0.8350\n"
          ]
        },
        {
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          "text": [
            "epoch 1 loss: 0.4447: 100%|██████████| 1070/1070 [01:12<00:00, 14.72it/s]\n",
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            "validation multi-class accuracy = 0.8451\n"
          ]
        },
        {
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          "text": [
            "epoch 2 loss: 0.4241: 100%|██████████| 1070/1070 [01:12<00:00, 14.76it/s]\n",
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          ]
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          "text": [
            "validation multi-class accuracy = 0.8411\n"
          ]
        },
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          "text": [
            "epoch 3 loss: 0.3763: 100%|██████████| 1070/1070 [01:12<00:00, 14.79it/s]\n",
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            "validation multi-class accuracy = 0.8385\n"
          ]
        },
        {
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          "text": [
            "epoch 4 loss: 0.3789: 100%|██████████| 1070/1070 [01:12<00:00, 14.74it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8495\n"
          ]
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          "text": [
            "epoch 5 loss: 0.3170: 100%|██████████| 1070/1070 [01:12<00:00, 14.71it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8551\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 6 loss: 0.3015: 100%|██████████| 1070/1070 [01:12<00:00, 14.76it/s]\n",
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          ]
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        {
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          "text": [
            "validation multi-class accuracy = 0.8523\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 7 loss: 0.3057: 100%|██████████| 1070/1070 [01:12<00:00, 14.76it/s]\n",
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          ]
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          "text": [
            "validation multi-class accuracy = 0.8612\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 8 loss: 0.2486: 100%|██████████| 1070/1070 [01:12<00:00, 14.74it/s]\n",
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          ]
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        {
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          "text": [
            "validation multi-class accuracy = 0.8602\n"
          ]
        },
        {
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          "text": [
            "epoch 9 loss: 0.2166: 100%|██████████| 1070/1070 [01:12<00:00, 14.72it/s]\n",
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          ]
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          "text": [
            "validation multi-class accuracy = 0.8605\n",
            "Training with 3 started\n",
            "17118 4279\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 0 loss: 0.4920: 100%|██████████| 1070/1070 [01:12<00:00, 14.70it/s]\n",
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            "validation multi-class accuracy = 0.8334\n"
          ]
        },
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          "text": [
            "epoch 1 loss: 0.4910: 100%|██████████| 1070/1070 [01:12<00:00, 14.75it/s]\n",
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            "validation multi-class accuracy = 0.8404\n"
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          "text": [
            "epoch 2 loss: 0.4516: 100%|██████████| 1070/1070 [01:12<00:00, 14.71it/s]\n",
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            "validation multi-class accuracy = 0.8231\n"
          ]
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            "validation multi-class accuracy = 0.8430\n"
          ]
        },
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          "text": [
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            "validation multi-class accuracy = 0.8504\n"
          ]
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          "text": [
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            "validation multi-class accuracy = 0.8439\n"
          ]
        },
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          "text": [
            "epoch 6 loss: 0.3252: 100%|██████████| 1070/1070 [01:12<00:00, 14.66it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8493\n"
          ]
        },
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          "name": "stderr",
          "text": [
            "epoch 7 loss: 0.2673: 100%|██████████| 1070/1070 [01:12<00:00, 14.70it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8532\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 8 loss: 0.2503: 100%|██████████| 1070/1070 [01:12<00:00, 14.70it/s]\n",
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          ]
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        {
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          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8619\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 9 loss: 0.2527: 100%|██████████| 1070/1070 [01:12<00:00, 14.74it/s]\n",
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          ]
        },
        {
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          "text": [
            "validation multi-class accuracy = 0.8614\n",
            "Training with 4 started\n",
            "17118 4279\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch 0 loss: 0.4903: 100%|██████████| 1070/1070 [01:12<00:00, 14.74it/s]\n",
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            "validation multi-class accuracy = 0.8301\n"
          ]
        },
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            "epoch 1 loss: 0.4831: 100%|██████████| 1070/1070 [01:12<00:00, 14.74it/s]\n",
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            "validation multi-class accuracy = 0.8317\n"
          ]
        },
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          "name": "stderr",
          "text": [
            "epoch 2 loss: 0.4389: 100%|██████████| 1070/1070 [01:12<00:00, 14.78it/s]\n",
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          ]
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        {
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          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8425\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 3 loss: 0.4043: 100%|██████████| 1070/1070 [01:12<00:00, 14.72it/s]\n",
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          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8581\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 4 loss: 0.3914: 100%|██████████| 1070/1070 [01:12<00:00, 14.77it/s]\n",
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          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8574\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 5 loss: 0.3432: 100%|██████████| 1070/1070 [01:12<00:00, 14.77it/s]\n",
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          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8481\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 6 loss: 0.3168: 100%|██████████| 1070/1070 [01:12<00:00, 14.79it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8640\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 7 loss: 0.2765: 100%|██████████| 1070/1070 [01:12<00:00, 14.75it/s]\n",
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          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8670\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 8 loss: 0.2567: 100%|██████████| 1070/1070 [01:12<00:00, 14.69it/s]\n",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "validation multi-class accuracy = 0.8687\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "epoch 9 loss: 0.2453: 100%|██████████| 1070/1070 [01:12<00:00, 14.78it/s]\n",
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          "text": [
            "validation multi-class accuracy = 0.8668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNvhIx-MKXQf"
      },
      "source": [
        "# Save Model Checkpoints to Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kv1fEsLWJ9rm"
      },
      "source": [
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_0 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_1 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_2 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_3 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_4 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_5 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_6 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_7 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_8 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/vit_base_patch16_224_fold_2_9 drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output/fold_2"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}