ViT_Inference.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ViT-Inference.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "2G_cjYBUoa6j",
        "k_3mXlLxKgyj"
      ],
      "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_Inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2G_cjYBUoa6j"
      },
      "source": [
        "# Check Resources"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UppodOH6oVqW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "feba186b-d755-4c21-e3a1-1d583c10bff6"
      },
      "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 10:49:20 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  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\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": {
        "id": "iij8E-2boeCA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd214dc9-70a9-47b1-e41c-129bdf65f350"
      },
      "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 54.8 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQ6_UM7kogRR"
      },
      "source": [
        "# Mount Your GDrive Here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzFR-YghoigE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ee0dfd9-b4d7-4855-c524-d76ca230d5f3"
      },
      "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": {
        "id": "KCWaxFCyokQ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32c0c226-7b33-4fea-d726-181f046659d7"
      },
      "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": "WaYWlZ1doli_"
      },
      "source": [
        "# Install Packages and Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHEF2peHonpH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8870ffea-bb9d-49fb-f409-4380d47136e4"
      },
      "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 175 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: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel) (5.3.5)\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: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel) (5.5.0)\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: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (4.4.2)\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: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipykernel) (4.8.0)\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: 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: 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: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel) (2.8.2)\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: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel) (22.3.0)\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 5.3 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 5.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.33.0 in /usr/local/lib/python3.7/dist-packages (from catalyst) (4.62.2)\n",
            "Collecting PyYAML>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 67.2 MB/s \n",
            "\u001b[?25hCollecting tensorboardX<2.3.0>=2.1.0\n",
            "  Downloading tensorboardX-2.2-py2.py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 69.6 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: 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": {
        "id": "b0qB3jt9opGb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5bfe284-8cb7-4b10-e160-3839122d40fd"
      },
      "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": {
        "id": "YhOetlbBor00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "410debcb-141f-409e-aa97-3a6a9d1aa647"
      },
      "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 3.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 37.1 MB 54 kB/s \n",
            "\u001b[?25halbumentations==1.0.3 is successfully installed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQjwrPdbEbCm",
        "outputId": "70521f3a-1704-4ae4-9e99-2513467c3d98"
      },
      "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 1.4 MB/s eta 0:13:40tcmalloc: large alloc 1147494400 bytes == 0x55d833d0c000 @  0x7fa6551b6615 0x55d7faa244cc 0x55d7fab0447a 0x55d7faa272ed 0x55d7fab18e1d 0x55d7faa9ae99 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa9ad00 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7faa29039 0x55d7faa6c409 0x55d7faa27c52 0x55d7faa9ac25 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96915 0x55d7faa28afa 0x55d7faa96c0d 0x55d7faa959ee\n",
            "\u001b[K     |█████████████████               | 1055.7 MB 1.3 MB/s eta 0:12:00tcmalloc: large alloc 1434370048 bytes == 0x55d878362000 @  0x7fa6551b6615 0x55d7faa244cc 0x55d7fab0447a 0x55d7faa272ed 0x55d7fab18e1d 0x55d7faa9ae99 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa9ad00 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7faa29039 0x55d7faa6c409 0x55d7faa27c52 0x55d7faa9ac25 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96915 0x55d7faa28afa 0x55d7faa96c0d 0x55d7faa959ee\n",
            "\u001b[K     |█████████████████████▋          | 1336.2 MB 1.4 MB/s eta 0:07:54tcmalloc: large alloc 1792966656 bytes == 0x55d7fd194000 @  0x7fa6551b6615 0x55d7faa244cc 0x55d7fab0447a 0x55d7faa272ed 0x55d7fab18e1d 0x55d7faa9ae99 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa9ad00 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7faa29039 0x55d7faa6c409 0x55d7faa27c52 0x55d7faa9ac25 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96915 0x55d7faa28afa 0x55d7faa96c0d 0x55d7faa959ee\n",
            "\u001b[K     |███████████████████████████▎    | 1691.1 MB 1.2 MB/s eta 0:03:57tcmalloc: large alloc 2241208320 bytes == 0x55d867f7c000 @  0x7fa6551b6615 0x55d7faa244cc 0x55d7fab0447a 0x55d7faa272ed 0x55d7fab18e1d 0x55d7faa9ae99 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa9ad00 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7fab19c66 0x55d7faa96daf 0x55d7faa29039 0x55d7faa6c409 0x55d7faa27c52 0x55d7faa9ac25 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96915 0x55d7faa28afa 0x55d7faa96c0d 0x55d7faa959ee\n",
            "\u001b[K     |████████████████████████████████| 1982.2 MB 1.2 MB/s eta 0:00:01tcmalloc: large alloc 1982251008 bytes == 0x55d8ed8de000 @  0x7fa6551b51e7 0x55d7faa5a067 0x55d7faa244cc 0x55d7fab0447a 0x55d7faa272ed 0x55d7fab18e1d 0x55d7faa9ae99 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa28afa 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee\n",
            "tcmalloc: large alloc 2477817856 bytes == 0x55d963b4a000 @  0x7fa6551b6615 0x55d7faa244cc 0x55d7fab0447a 0x55d7faa272ed 0x55d7fab18e1d 0x55d7faa9ae99 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa96c0d 0x55d7faa28afa 0x55d7faa96c0d 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee 0x55d7faa28bda 0x55d7faa97737 0x55d7faa959ee 0x55d7faa29271\n",
            "\u001b[K     |████████████████████████████████| 1982.2 MB 3.6 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 10 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": "nnxpRbrxouDH"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiBpEBjpovg4"
      },
      "source": [
        "## Pre-setting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcTm9TRdoyg3"
      },
      "source": [
        "package_path = 'input/pytorch-image-models/pytorch-image-models-master'\n",
        "import sys; sys.path.append(package_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kabTX5nUo2s6"
      },
      "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",
        "\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",
        "import timm #from efficientnet_pytorch import EfficientNet\n",
        "from scipy.ndimage.interpolation import zoom\n",
        "from sklearn.metrics import log_loss\n",
        "\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": "piBNXoJoo_fY"
      },
      "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': 32,\n",
        "    'valid_bs': 32,\n",
        "    'lr': 1e-4,\n",
        "    'num_workers': 4,\n",
        "    'accum_iter': 1, # suppoprt to do batch accumulation for backprop with effectively larger batch size\n",
        "    'verbose_step': 1,\n",
        "    'device': 'cuda:0',\n",
        "    'tta': 3,\n",
        "    'used_epochs': [6,7,8,9],\n",
        "    'weights': [1,1,1,1]\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cds9-DZZpCJI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "dc707103-e31f-4019-c6b0-f59370928480"
      },
      "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": {
        "id": "MlUl1bMDpEpu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f399e90-6f77-4474-aae2-a5f01ad444c4"
      },
      "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": {
        "id": "Ee8pCPrjpF1W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "0bd949b2-c5e0-48fa-d125-7d622582072a"
      },
      "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": "__GT6zJwpHyK"
      },
      "source": [
        "## Helper Functions (perform this twice)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "dYX9VrKwxyCl",
        "outputId": "126a60e1-49ef-4613-91f8-f965317a97fe"
      },
      "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 = plt.imread(pathw)\n",
        "    im_rgb = im_bgr\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": "WGuxGrhbpNVw"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StRGAD27pOKN"
      },
      "source": [
        "class CassavaDataset(Dataset):\n",
        "    def __init__(\n",
        "        self, df, data_root, transforms=None, output_label=True\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.output_label = output_label\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.df.iloc[index]['label']\n",
        "          \n",
        "        path = \"{}/{}\".format(self.data_root, self.df.iloc[index]['image_id'])\n",
        "        \n",
        "        img  = get_img(path)\n",
        "        \n",
        "        if self.transforms:\n",
        "            img = self.transforms(image=img)['image']\n",
        "            \n",
        "        # do label smoothing\n",
        "        if self.output_label == True:\n",
        "            return img, target\n",
        "        else:\n",
        "            return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vegHNj0Tpc5i"
      },
      "source": [
        "## Define Training and Validation Image Augmentations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Z_K0wA5pf45"
      },
      "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.)\n",
        "\n",
        "def get_inference_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",
        "            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",
        "            ToTensorV2(p=1.0),\n",
        "        ], p=1.)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXyHB4VMpp8v"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZvr9stDpqxR"
      },
      "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": "R23YnyT7pv4N"
      },
      "source": [
        "## Main Inference Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coUjdmENpwyJ"
      },
      "source": [
        "def inference_one_epoch(model, data_loader, device):\n",
        "    model.eval()\n",
        "\n",
        "    image_preds_all = []\n",
        "    \n",
        "    pbar = tqdm(enumerate(data_loader), total=len(data_loader))\n",
        "    for step, (imgs) in pbar:\n",
        "        imgs = imgs.to(device).float()\n",
        "        \n",
        "        image_preds = model(imgs)   #output = model(input)\n",
        "        image_preds_all += [torch.softmax(image_preds, 1).detach().cpu().numpy()]\n",
        "        \n",
        "    \n",
        "    image_preds_all = np.concatenate(image_preds_all, axis=0)\n",
        "    return image_preds_all"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tStnSp5Xp07h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca1d33e5-15b3-44fa-ad73-8e98a037852f"
      },
      "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']).split(np.arange(train.shape[0]), train.label.values)\n",
        "    \n",
        "    for fold, (trn_idx, val_idx) in enumerate(folds):\n",
        "        print('Inference with fold {} started'.format(fold))\n",
        "\n",
        "        valid_ = train.loc[val_idx,:].reset_index(drop=True)\n",
        "        valid_ds = CassavaDataset(valid_, 'input/cassava-leaf-disease-classification/train_images/', transforms=get_inference_transforms(), output_label=False)\n",
        "        \n",
        "        test = pd.DataFrame()\n",
        "        test['image_id'] = list(os.listdir('input/cassava-leaf-disease-classification/test_images/'))\n",
        "        test_ds = CassavaDataset(test, 'input/cassava-leaf-disease-classification/test_images/', transforms=get_inference_transforms(), output_label=False)\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",
        "        \n",
        "        tst_loader = torch.utils.data.DataLoader(\n",
        "            test_ds, \n",
        "            batch_size=CFG['valid_bs'],\n",
        "            num_workers=CFG['num_workers'],\n",
        "            shuffle=False,\n",
        "            pin_memory=False,\n",
        "        )\n",
        "        val_preds = []\n",
        "        tst_preds = []\n",
        "        \n",
        "        device = torch.device(CFG['device'])\n",
        "        model = CassvaImgClassifier(CFG['model_arch'], train.label.nunique()).to(device)\n",
        "\n",
        "        #for epoch in range(CFG['epochs']-3):\n",
        "        for i, epoch in enumerate(CFG['used_epochs']):    \n",
        "            model.load_state_dict(torch.load('output/{}_fold_{}_{}'.format(CFG['model_arch'], fold, epoch)))\n",
        "            \n",
        "            with torch.no_grad():\n",
        "                for _ in range(CFG['tta']):\n",
        "                    val_preds += [CFG['weights'][i]/sum(CFG['weights'])/CFG['tta']*inference_one_epoch(model, val_loader, device)]\n",
        "                    tst_preds += [CFG['weights'][i]/sum(CFG['weights'])/CFG['tta']*inference_one_epoch(model, tst_loader, device)]\n",
        "\n",
        "        val_preds = np.mean(val_preds, axis=0) \n",
        "        tst_preds = np.mean(tst_preds, axis=0) \n",
        "        \n",
        "        print('\\nfold {} validation loss = {:.5f}'.format(fold, log_loss(valid_.label.values, val_preds)))\n",
        "        print('fold {} validation accuracy = {:.5f}\\n'.format(fold, (valid_.label.values==np.argmax(val_preds, axis=1)).mean()))\n",
        "        \n",
        "        del model\n",
        "        torch.cuda.empty_cache()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inference with fold 0 started\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "fold 0 validation loss = 0.22266\n",
            "fold 0 validation accuracy = 0.93061\n",
            "\n",
            "Inference with fold 1 started\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "fold 1 validation loss = 0.22962\n",
            "fold 1 validation accuracy = 0.93154\n",
            "\n",
            "Inference with fold 2 started\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "fold 2 validation loss = 0.20858\n",
            "fold 2 validation accuracy = 0.93877\n",
            "\n",
            "Inference with fold 3 started\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "fold 3 validation loss = 0.23106\n",
            "fold 3 validation accuracy = 0.93410\n",
            "\n",
            "Inference with fold 4 started\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "fold 4 validation loss = 0.21640\n",
            "fold 4 validation accuracy = 0.93573\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gK_eMDQzp_-1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "b61719d9-b5de-4e1c-b30f-85b83317ea3b"
      },
      "source": [
        "test['label'] = np.argmax(tst_preds, axis=1)\n",
        "test.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>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         image_id  label\n",
              "0  2216849948.jpg      2"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ht8YbsWqBqF"
      },
      "source": [
        "test.to_csv('output/submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_3mXlLxKgyj"
      },
      "source": [
        "# Save Submission CSV to Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaOx4SrWG7Fh",
        "outputId": "0296fa19-7ac1-4083-93ec-2432fa40f3e9"
      },
      "source": [
        "%cd ..\n",
        "\n",
        "!cp -r /content/Cassava-Leaf-Disease/output/submission.csv drive/MyDrive/Kaggle-Projects/Cassava-Leaf-Disease/output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    }
  ]
}