Domain_Adaptation_With_Domain_Adversarial_Training_of_Neural_Networks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reshalfahsi/domain-adaptation/blob/master/Domain_Adaptation_With_Domain_Adversarial_Training_of_Neural_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NfOUGj4lpc7"
      },
      "source": [
        "# **Domain Adaptation With Domain-Adversarial Training of Neural Networks**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bn6rOS6Yls4f"
      },
      "source": [
        "## **Important Libraries**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKzqAlqRmjJB"
      },
      "source": [
        "### **Install**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6XmVFs2dlfO7",
        "outputId": "de6ce0d9-6fb0-4ea9-ef93-995f5af5e43a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.8/2.1 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m48.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m840.4/840.4 kB\u001b[0m \u001b[31m162.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m801.6/801.6 kB\u001b[0m \u001b[31m146.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q --no-cache-dir lightning torchmetrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8KYLw9Avltiv"
      },
      "source": [
        "### **Import**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "1kC01U94ltom"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    import lightning as L\n",
        "except:\n",
        "    import lightning as L\n",
        "\n",
        "from lightning.pytorch import Trainer, seed_everything\n",
        "from lightning.pytorch.callbacks import ModelCheckpoint\n",
        "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n",
        "\n",
        "from torchmetrics.classification import Accuracy\n",
        "\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as data\n",
        "\n",
        "from torch.autograd import Function\n",
        "\n",
        "from torchvision.transforms.v2 import (\n",
        "    Compose,\n",
        "    ToTensor,\n",
        "    Resize,\n",
        "    Lambda,\n",
        "    RandomInvert,\n",
        "    RandomAffine,\n",
        "    RandomChannelPermutation,\n",
        "    RandomPhotometricDistort\n",
        ")\n",
        "from torchvision.datasets import SVHN, VisionDataset\n",
        "from torchvision.datasets.utils import (\n",
        "    _flip_byte_order,\n",
        "    check_integrity,\n",
        "    download_url,\n",
        "    download_and_extract_archive\n",
        ")\n",
        "from torchvision.models import (\n",
        "    regnet_y_400mf,\n",
        "    RegNet_Y_400MF_Weights\n",
        ")\n",
        "\n",
        "from urllib.error import URLError\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.io as sio\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "from functools import partial\n",
        "\n",
        "import codecs\n",
        "\n",
        "import os\n",
        "import sys\n",
        "import cv2\n",
        "import math\n",
        "import copy\n",
        "import time\n",
        "import random\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "%matplotlib inline\n",
        "plt.rcParams['axes.facecolor'] = 'lightgray'\n",
        "plt.rcParams['mathtext.fontset'] = 'cm'\n",
        "plt.rcParams['font.family'] = 'STIXGeneral'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UGg5It9lt-1"
      },
      "source": [
        "## **Configuration**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "M5W27BZDvU6a"
      },
      "outputs": [],
      "source": [
        "IMAGE_SIZE = 32\n",
        "BATCH_SIZE = 100\n",
        "EPOCH = 50\n",
        "NUM_CLASSES = 10\n",
        "LEARNING_RATE = 1e-3\n",
        "BASE_LR_SCALE = 1e-4\n",
        "EARLY_STOPPING_PATIENCE = 1e-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2fL_84OQBFhb"
      },
      "outputs": [],
      "source": [
        "METRIC_TO_MONITOR = \"val_acc\"\n",
        "METRIC_MODE       = \"max\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "lb41D7ASluCe"
      },
      "outputs": [],
      "source": [
        "os.makedirs(\"experiment\", exist_ok=True)\n",
        "os.makedirs(\"experiment/training\", exist_ok=True)\n",
        "os.makedirs(\"experiment/dataset\", exist_ok=True)\n",
        "os.makedirs(\"experiment/model\", exist_ok=True)\n",
        "EXPERIMENT_DIR = \"experiment/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8J3vt-eCnt2Z",
        "outputId": "e0f68f51-77bd-4781-c745-1fba9767ad81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random seed: 1500186762\n"
          ]
        }
      ],
      "source": [
        "SEED = int(np.random.randint(2147483647))\n",
        "print(f\"Random seed: {SEED}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGe2laAvluJs"
      },
      "source": [
        "## **Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfSTO7GrqL-E"
      },
      "source": [
        "### **Utils**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "P2j0gu33cWXW"
      },
      "outputs": [],
      "source": [
        "TRANSFORM = Compose(\n",
        "    [\n",
        "        Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
        "        ToTensor(),\n",
        "        Lambda(lambda x: (x * 2) - 1),\n",
        "    ]\n",
        ")\n",
        "TRANSFORM_AUGMENTATION = Compose(\n",
        "    [\n",
        "        RandomAffine(\n",
        "            degrees=90.,\n",
        "            translate=(0.1, 0.1),\n",
        "            scale=(0.69, 1.414),\n",
        "            shear=10.,\n",
        "        ),\n",
        "        RandomInvert(),\n",
        "        RandomPhotometricDistort(),\n",
        "        Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
        "        RandomChannelPermutation(),\n",
        "        ToTensor(),\n",
        "        Lambda(lambda x: (x * 2) - 1),\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "s___IkrrAUCN"
      },
      "outputs": [],
      "source": [
        "def get_int(b: bytes) -> int:\n",
        "    return int(codecs.encode(b, \"hex\"), 16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "bgrvkO1HAE_A"
      },
      "outputs": [],
      "source": [
        "SN3_PASCALVINCENT_TYPEMAP = {\n",
        "    8: torch.uint8,\n",
        "    9: torch.int8,\n",
        "    11: torch.int16,\n",
        "    12: torch.int32,\n",
        "    13: torch.float32,\n",
        "    14: torch.float64,\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6HtU9ASo_i4e"
      },
      "outputs": [],
      "source": [
        "def read_sn3_pascalvincent_tensor(\n",
        "    path: str,\n",
        "    strict: bool = True,\n",
        ") -> torch.Tensor:\n",
        "    \"\"\"Read a SN3 file in \"Pascal Vincent\" format\n",
        "    (Lush file 'libidx/idx-io.lsh'). Argument may be a filename,\n",
        "    compressed filename, or file object.\n",
        "    \"\"\"\n",
        "    # read\n",
        "    with open(path, \"rb\") as f:\n",
        "        data = f.read()\n",
        "\n",
        "    # parse\n",
        "    if sys.byteorder == \"little\":\n",
        "        magic = get_int(data[0:4])\n",
        "        nd = magic % 256\n",
        "        ty = magic // 256\n",
        "    else:\n",
        "        nd = get_int(data[0:1])\n",
        "        ty = (\n",
        "            get_int(data[1:2])\n",
        "            + get_int(data[2:3]) * 256\n",
        "            + get_int(data[3:4]) * 256 * 256\n",
        "        )\n",
        "\n",
        "    assert 1 <= nd <= 3\n",
        "    assert 8 <= ty <= 14\n",
        "    torch_type = SN3_PASCALVINCENT_TYPEMAP[ty]\n",
        "    s = [get_int(data[4 * (i + 1) : 4 * (i + 2)]) for i in range(nd)]\n",
        "\n",
        "    if sys.byteorder == \"big\":\n",
        "        for i in range(len(s)):\n",
        "            s[i] = int.from_bytes(\n",
        "                s[i].to_bytes(4, byteorder=\"little\"),\n",
        "                byteorder=\"big\",\n",
        "                signed=False,\n",
        "            )\n",
        "\n",
        "    parsed = torch.frombuffer(\n",
        "        bytearray(data), dtype=torch_type, offset=(4 * (nd + 1))\n",
        "    )\n",
        "\n",
        "    # The MNIST format uses the big endian byte order, while `torch.frombuffer`\n",
        "    # uses whatever the system uses. In case that is little endian and the dtype\n",
        "    # has more than one byte, we need to flip them.\n",
        "    if sys.byteorder == \"little\" and parsed.element_size() > 1:\n",
        "        parsed = _flip_byte_order(parsed)\n",
        "\n",
        "    assert parsed.shape[0] == np.prod(s) or not strict\n",
        "    return parsed.view(*s)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "iNZwjP9K_aDM"
      },
      "outputs": [],
      "source": [
        "def read_image_file(path: str) -> torch.Tensor:\n",
        "    x = read_sn3_pascalvincent_tensor(path, strict=False)\n",
        "    if x.dtype != torch.uint8:\n",
        "        raise TypeError(\n",
        "            f\"x should be of dtype torch.uint8 instead of {x.dtype}\"\n",
        "        )\n",
        "    if x.ndimension() != 3:\n",
        "        raise ValueError(\n",
        "            f\"x should have 3 dimension instead of {x.ndimension()}\"\n",
        "        )\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "RDv3YveeAavO"
      },
      "outputs": [],
      "source": [
        "def read_label_file(path: str) -> torch.Tensor:\n",
        "    x = read_sn3_pascalvincent_tensor(path, strict=False)\n",
        "    if x.dtype != torch.uint8:\n",
        "        raise TypeError(\n",
        "            f\"x should be of dtype torch.uint8 instead of {x.dtype}\"\n",
        "        )\n",
        "    if x.ndimension() != 1:\n",
        "        raise ValueError(\n",
        "            f\"x should have 1 dimension instead of {x.ndimension()}\"\n",
        "        )\n",
        "    return x.long()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "OVPo0_HD0jMV"
      },
      "outputs": [],
      "source": [
        "class DomainAdaptationDataset(VisionDataset):\n",
        "    mirrors = [\n",
        "        \"http://yann.lecun.com/exdb/mnist/\",\n",
        "        \"https://ossci-datasets.s3.amazonaws.com/mnist/\",\n",
        "    ]\n",
        "\n",
        "    resources = [\n",
        "        (\"train-images-idx3-ubyte.gz\", \"f68b3c2dcbeaaa9fbdd348bbdeb94873\"),\n",
        "        (\"train-labels-idx1-ubyte.gz\", \"d53e105ee54ea40749a09fcbcd1e9432\"),\n",
        "    ]\n",
        "\n",
        "    split_list = [\n",
        "        \"http://ufldl.stanford.edu/housenumbers/train_32x32.mat\",\n",
        "        \"train_32x32.mat\",\n",
        "        \"e26dedcc434d2e4c54c9b2d4a06d8373\",\n",
        "    ]\n",
        "\n",
        "    def __init__(self, root: str) -> None:\n",
        "        super().__init__(root)\n",
        "\n",
        "        self.url = self.split_list[0]\n",
        "        self.filename = self.split_list[1]\n",
        "        self.file_md5 = self.split_list[2]\n",
        "\n",
        "        self.download_mnist()\n",
        "        self.download_svhn()\n",
        "\n",
        "        if not self._check_exists_mnist and not self._check_integrity_svhn:\n",
        "            raise RuntimeError(\n",
        "                \"Dataset not found. You can use download=True to download it\"\n",
        "            )\n",
        "\n",
        "        self.data_mnist, self.labels_mnist = self._load_data_mnist()\n",
        "\n",
        "        loaded_mat = sio.loadmat(os.path.join(self.root, self.filename))\n",
        "        self.data_svhn = np.transpose(loaded_mat[\"X\"], (3, 2, 0, 1))\n",
        "\n",
        "    def _load_data_mnist(self):\n",
        "        image_file = f\"train-images-idx3-ubyte\"\n",
        "        data = read_image_file(os.path.join(self.raw_folder, image_file))\n",
        "\n",
        "        label_file = f\"train-labels-idx1-ubyte\"\n",
        "        targets = read_label_file(os.path.join(self.raw_folder, label_file))\n",
        "\n",
        "        return data, targets\n",
        "\n",
        "    def __getitem__(self, index: int):\n",
        "        index_mnist = (\n",
        "            index % len(self.data_mnist)\n",
        "            if index > (len(self.data_mnist) - 1)\n",
        "            else index\n",
        "        )\n",
        "        img_mnist = self.data_mnist[index_mnist]\n",
        "        img_svhn = self.data_svhn[index]\n",
        "\n",
        "        label_mnist = int(self.labels_mnist[index_mnist])\n",
        "\n",
        "        img_mnist = Image.fromarray(img_mnist.numpy(), mode=\"L\").convert(\"RGB\")\n",
        "        img_svhn = Image.fromarray(np.transpose(img_svhn, (1, 2, 0)))\n",
        "\n",
        "        img_mnist = TRANSFORM_AUGMENTATION(img_mnist)\n",
        "        img_svhn = TRANSFORM(img_svhn)\n",
        "\n",
        "        return img_mnist, img_svhn, label_mnist\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return len(self.data_svhn)\n",
        "\n",
        "    @property\n",
        "    def raw_folder(self) -> str:\n",
        "        return os.path.join(self.root, self.__class__.__name__, \"raw\")\n",
        "\n",
        "    @property\n",
        "    def processed_folder(self) -> str:\n",
        "        return os.path.join(self.root, self.__class__.__name__, \"processed\")\n",
        "\n",
        "    @property\n",
        "    def _check_exists_mnist(self) -> bool:\n",
        "        return all(\n",
        "            check_integrity(\n",
        "                os.path.join(\n",
        "                    self.raw_folder, os.path.splitext(os.path.basename(url))[0]\n",
        "                )\n",
        "            )\n",
        "            for url, _ in self.resources\n",
        "        )\n",
        "\n",
        "    @property\n",
        "    def _check_integrity_svhn(self) -> bool:\n",
        "        return check_integrity(\n",
        "            os.path.join(self.root, self.filename),\n",
        "            self.file_md5,\n",
        "        )\n",
        "\n",
        "    def download_svhn(self) -> None:\n",
        "        if self._check_integrity_svhn:\n",
        "            return\n",
        "\n",
        "        download_url(self.url, self.root, self.filename, self.file_md5)\n",
        "\n",
        "    def download_mnist(self) -> None:\n",
        "        \"\"\"Download the MNIST data if it doesn't exist already.\"\"\"\n",
        "\n",
        "        if self._check_exists_mnist:\n",
        "            return\n",
        "\n",
        "        os.makedirs(self.raw_folder, exist_ok=True)\n",
        "\n",
        "        # download files\n",
        "        for filename, md5 in self.resources:\n",
        "            for mirror in self.mirrors:\n",
        "                url = f\"{mirror}{filename}\"\n",
        "                try:\n",
        "                    print(f\"Downloading {url}\")\n",
        "                    download_and_extract_archive(\n",
        "                        url,\n",
        "                        download_root=self.raw_folder,\n",
        "                        filename=filename,\n",
        "                        md5=md5,\n",
        "                    )\n",
        "                except URLError as error:\n",
        "                    print(f\"Failed to download (trying next):\\n{error}\")\n",
        "                    continue\n",
        "                finally:\n",
        "                    print()\n",
        "                break\n",
        "            else:\n",
        "                raise RuntimeError(f\"Error downloading {filename}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v72HiXPUlqMX"
      },
      "source": [
        "### **Load**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8lBAk5wlqSd",
        "outputId": "7b6cd30d-5350-4f87-c711-cedf05702108"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to experiment/dataset/DomainAdaptationDataset/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 419664186.01it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting experiment/dataset/DomainAdaptationDataset/raw/train-images-idx3-ubyte.gz to experiment/dataset/DomainAdaptationDataset/raw\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to experiment/dataset/DomainAdaptationDataset/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 30095824.55it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting experiment/dataset/DomainAdaptationDataset/raw/train-labels-idx1-ubyte.gz to experiment/dataset/DomainAdaptationDataset/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://ufldl.stanford.edu/housenumbers/train_32x32.mat to experiment/dataset/train_32x32.mat\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 182040794/182040794 [00:45<00:00, 3966788.33it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://ufldl.stanford.edu/housenumbers/test_32x32.mat to experiment/dataset/test_32x32.mat\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 64275384/64275384 [00:15<00:00, 4178314.30it/s]\n"
          ]
        }
      ],
      "source": [
        "TrainDataset = DomainAdaptationDataset(root=\"experiment/dataset\")\n",
        "ValDataset = SVHN(\n",
        "    root=\"experiment/dataset\",\n",
        "    split=\"test\",\n",
        "    transform=TRANSFORM,\n",
        "    download=True,\n",
        ")\n",
        "TestDataset = ValDataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMljZV18VuJB"
      },
      "source": [
        "## **Utils**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "W77g0UBoVuRl"
      },
      "outputs": [],
      "source": [
        "class AvgMeter(object):\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.scores = list()\n",
        "\n",
        "    def update(self, val):\n",
        "        self.scores.append(val)\n",
        "\n",
        "    def show(self):\n",
        "        scores = torch.stack(self.scores)\n",
        "        return torch.mean(scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ow0UIr5wlqV0"
      },
      "source": [
        "## **Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "sXjmxDRssDaM"
      },
      "outputs": [],
      "source": [
        "class GradientReversalLayer(Function):\n",
        "    @staticmethod\n",
        "    def forward(ctx, x):\n",
        "        return x.view_as(x)\n",
        "\n",
        "    @staticmethod\n",
        "    def backward(ctx, grad_output):\n",
        "        output = grad_output.neg()\n",
        "        return output, None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "vxgV3dRKpezq"
      },
      "outputs": [],
      "source": [
        "class DANN(L.LightningModule):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        self.batch_size = BATCH_SIZE\n",
        "        self.max_epoch = EPOCH\n",
        "        self.lr = LEARNING_RATE\n",
        "        self.lr_now = self.lr * 1e3\n",
        "\n",
        "        self.backbone = regnet_y_400mf(\n",
        "            weights=RegNet_Y_400MF_Weights.DEFAULT,\n",
        "        )\n",
        "        self.backbone.fc = nn.Sequential(\n",
        "            nn.Linear(440, 110, bias=False),\n",
        "            nn.BatchNorm1d(110),\n",
        "            nn.ReLU(True),\n",
        "            nn.Dropout(p=0.4),\n",
        "            nn.Linear(110, 110, bias=False),\n",
        "            nn.BatchNorm1d(110),\n",
        "            nn.ReLU(True),\n",
        "            nn.Dropout(p=0.4),\n",
        "            nn.Linear(110, 10),\n",
        "        )\n",
        "\n",
        "        self.domain_classifier = nn.Sequential(\n",
        "            nn.Linear(440, 110, bias=False),\n",
        "            nn.BatchNorm1d(110),\n",
        "            nn.ReLU(True),\n",
        "            nn.Dropout(p=0.4),\n",
        "            nn.Linear(110, 2),\n",
        "        )\n",
        "\n",
        "        self.automatic_optimization = False\n",
        "\n",
        "        self.train_accuracy = Accuracy(\n",
        "            task=\"multiclass\",\n",
        "            num_classes=NUM_CLASSES,\n",
        "        )\n",
        "\n",
        "        self.val_accuracy = Accuracy(\n",
        "            task=\"multiclass\",\n",
        "            num_classes=NUM_CLASSES,\n",
        "        )\n",
        "\n",
        "        self.test_accuracy = Accuracy(\n",
        "            task=\"multiclass\",\n",
        "            num_classes=NUM_CLASSES,\n",
        "        )\n",
        "\n",
        "        self.train_loss = list()\n",
        "        self.val_loss = list()\n",
        "\n",
        "        self.train_acc = list()\n",
        "        self.val_acc = list()\n",
        "\n",
        "        self.train_loss_recorder = AvgMeter()\n",
        "        self.val_loss_recorder = AvgMeter()\n",
        "\n",
        "        self.train_acc_recorder = AvgMeter()\n",
        "        self.val_acc_recorder = AvgMeter()\n",
        "\n",
        "        self.sanity_check_counter = 1\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.backbone.stem(x)\n",
        "        x = self.backbone.trunk_output(x)\n",
        "\n",
        "        x = self.backbone.avgpool(x)\n",
        "        x = x.flatten(start_dim=1)\n",
        "\n",
        "        x_class = self.backbone.fc(x)\n",
        "\n",
        "        if not self.training:\n",
        "            return x_class\n",
        "\n",
        "        x_reverse = GradientReversalLayer.apply(x)\n",
        "        x_domain = self.domain_classifier(x_reverse)\n",
        "\n",
        "        return x_class, x_domain\n",
        "\n",
        "    def training_step(self, batch, batch_nb):\n",
        "        source_images, target_images, source_labels = batch\n",
        "\n",
        "        source_labels = source_labels.view(source_labels.shape[0])\n",
        "\n",
        "        class_predictions, domain_predictions = self(source_images)\n",
        "\n",
        "        self.train_accuracy.update(class_predictions.softmax(1), source_labels)\n",
        "        acc = self.train_accuracy.compute().data.cpu()\n",
        "\n",
        "        domain_labels = torch.zeros(\n",
        "            source_images.size(0)\n",
        "        ).long().to(self.device)\n",
        "\n",
        "        class_loss = F.cross_entropy(class_predictions, source_labels)\n",
        "        source_domain_loss = F.cross_entropy(domain_predictions, domain_labels)\n",
        "\n",
        "        domain_labels = torch.ones(\n",
        "            source_images.size(0)\n",
        "        ).long().to(self.device)\n",
        "\n",
        "        _, domain_predictions = self(target_images)\n",
        "        target_domain_loss = F.cross_entropy(domain_predictions, domain_labels)\n",
        "\n",
        "        opt = self.optimizers()\n",
        "        opt.zero_grad()\n",
        "        loss = (\n",
        "            class_loss\n",
        "            + source_domain_loss\n",
        "            + target_domain_loss\n",
        "        )\n",
        "        self.manual_backward(loss)\n",
        "        opt.step()\n",
        "\n",
        "        self.log(\"train_loss\", loss, prog_bar=True)\n",
        "        self.log(\"train_acc\", acc, prog_bar=True)\n",
        "\n",
        "        self.train_loss_recorder.update(loss.data)\n",
        "        self.train_acc_recorder.update(acc)\n",
        "\n",
        "        sch = self.lr_schedulers()\n",
        "        sch.step()\n",
        "\n",
        "    def on_train_epoch_end(self):\n",
        "        self.train_loss.append(\n",
        "            self.train_loss_recorder.show().data.cpu().numpy()\n",
        "        )\n",
        "        self.train_loss_recorder = AvgMeter()\n",
        "\n",
        "        self.train_acc.append(self.train_acc_recorder.show().data.cpu().numpy())\n",
        "        self.train_acc_recorder = AvgMeter()\n",
        "\n",
        "    def validation_step(self, batch, batch_nb):\n",
        "        x, y = batch\n",
        "        y_hat = self(x)\n",
        "        y = y.view(y.shape[0])\n",
        "        loss = F.cross_entropy(y_hat, y)\n",
        "\n",
        "        if self.sanity_check_counter == 0:\n",
        "            self.val_accuracy.update(y_hat.softmax(1), y)\n",
        "            acc = self.val_accuracy.compute().data.cpu()\n",
        "\n",
        "            self.log(\"val_loss\", loss, prog_bar=True)\n",
        "            self.log(\"val_acc\", acc, prog_bar=True)\n",
        "\n",
        "            self.val_loss_recorder.update(loss.data)\n",
        "            self.val_acc_recorder.update(acc)\n",
        "\n",
        "    def on_validation_epoch_end(self):\n",
        "        if self.sanity_check_counter == 0:\n",
        "            self.val_loss.append(\n",
        "                self.val_loss_recorder.show().data.cpu().numpy()\n",
        "            )\n",
        "            self.val_loss_recorder = AvgMeter()\n",
        "            acc = self.val_acc_recorder.show().data.cpu().numpy()\n",
        "            lr_now_ = self.optimizers().param_groups[0][\"lr\"]\n",
        "            if self.lr_now != lr_now_:\n",
        "                self.lr_now = lr_now_\n",
        "                report_str = f\"[{MODEL_NAME}] Learning Rate Changed:\"\n",
        "                report_str += f\" {lr_now_} - Epoch: {self.current_epoch}\"\n",
        "                print(report_str)\n",
        "            self.val_acc.append(acc)\n",
        "            self.val_acc_recorder = AvgMeter()\n",
        "        else:\n",
        "            self.sanity_check_counter -= 1\n",
        "\n",
        "    def test_step(self, batch, batch_nb):\n",
        "        x, y = batch\n",
        "        y_hat = self(x)\n",
        "        y = y.view(y.shape[0])\n",
        "        loss = F.cross_entropy(y_hat, y)\n",
        "        self.test_accuracy.update(y_hat.softmax(1), y)\n",
        "\n",
        "        self.log(\"test_loss\", loss, prog_bar=True, logger=True)\n",
        "        self.log(\n",
        "            \"test_acc\",\n",
        "            self.test_accuracy.compute(),\n",
        "            prog_bar=True,\n",
        "            logger=True,\n",
        "        )\n",
        "\n",
        "    def on_train_end(self):\n",
        "        # Loss\n",
        "        loss_img_file = (\n",
        "            f\"experiment/training/{MODEL_NAME}_loss_plot.png\"\n",
        "        )\n",
        "        plt.plot(self.train_loss, color=\"r\", label=\"train\")\n",
        "        plt.plot(self.val_loss, color=\"b\", label=\"validation\")\n",
        "        plt.title(\"Loss Curves\")\n",
        "        plt.xlabel(\"Epoch\")\n",
        "        plt.ylabel(\"Loss\")\n",
        "        plt.legend()\n",
        "        plt.grid()\n",
        "        plt.savefig(loss_img_file)\n",
        "        plt.clf()\n",
        "        img = cv2.imread(loss_img_file)\n",
        "        cv2_imshow(img)\n",
        "\n",
        "        # Accuracy\n",
        "        acc_img_file = (\n",
        "            f\"experiment/training/{MODEL_NAME}_acc_plot.png\"\n",
        "        )\n",
        "        plt.plot(self.train_acc, color=\"r\", label=\"train\")\n",
        "        plt.plot(self.val_acc, color=\"b\", label=\"validation\")\n",
        "        plt.title(\"Accuracy Curves\")\n",
        "        plt.xlabel(\"Epoch\")\n",
        "        plt.ylabel(\"Accuracy\")\n",
        "        plt.legend()\n",
        "        plt.grid()\n",
        "        plt.savefig(acc_img_file)\n",
        "        plt.clf()\n",
        "        img = cv2.imread(acc_img_file)\n",
        "        cv2_imshow(img)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return data.DataLoader(\n",
        "            dataset=TrainDataset,\n",
        "            batch_size=self.batch_size,\n",
        "            shuffle=True,\n",
        "        )\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return data.DataLoader(\n",
        "            dataset=ValDataset,\n",
        "            batch_size=self.batch_size,\n",
        "            shuffle=False,\n",
        "        )\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        return data.DataLoader(\n",
        "            dataset=TestDataset,\n",
        "            batch_size=1,\n",
        "            shuffle=False,\n",
        "        )\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = optim.Adam(\n",
        "            self.parameters(),\n",
        "            lr=self.lr,\n",
        "        )\n",
        "        lr_scheduler = {\n",
        "            \"scheduler\": optim.lr_scheduler.CyclicLR(\n",
        "                optimizer,\n",
        "                base_lr=BASE_LR_SCALE * self.lr,\n",
        "                max_lr=self.lr,\n",
        "                cycle_momentum=False,\n",
        "            ),\n",
        "            \"name\": \"lr_scheduler\",\n",
        "        }\n",
        "\n",
        "        return [optimizer], [lr_scheduler]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Ni2vVs_OsaSJ"
      },
      "outputs": [],
      "source": [
        "MODEL = DANN\n",
        "MODEL_NAME = DANN.__name__\n",
        "BEST_MODEL_PATH = os.path.join(\n",
        "    EXPERIMENT_DIR,\n",
        "    \"model\",\n",
        "    f\"{MODEL_NAME}_best.ckpt\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vxG6Uzflqjl"
      },
      "source": [
        "## **Training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          "name": "stderr",
          "text": [
            "INFO: Seed set to 1500186762\n",
            "INFO:lightning.fabric.utilities.seed:Seed set to 1500186762\n"
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          "text": [
            "DANN\n"
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          "text": [
            "Downloading: \"https://download.pytorch.org/models/regnet_y_400mf-e6988f5f.pth\" to /root/.cache/torch/hub/checkpoints/regnet_y_400mf-e6988f5f.pth\n",
            "100%|██████████| 16.8M/16.8M [00:01<00:00, 9.65MB/s]\n",
            "INFO: GPU available: True (cuda), used: True\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO: \n",
            "  | Name              | Type               | Params\n",
            "---------------------------------------------------------\n",
            "0 | backbone          | RegNet             | 4.0 M \n",
            "1 | domain_classifier | Sequential         | 48.8 K\n",
            "2 | train_accuracy    | MulticlassAccuracy | 0     \n",
            "3 | val_accuracy      | MulticlassAccuracy | 0     \n",
            "4 | test_accuracy     | MulticlassAccuracy | 0     \n",
            "---------------------------------------------------------\n",
            "4.0 M     Trainable params\n",
            "0         Non-trainable params\n",
            "4.0 M     Total params\n",
            "16.056    Total estimated model params size (MB)\n",
            "INFO:lightning.pytorch.callbacks.model_summary:\n",
            "  | Name              | Type               | Params\n",
            "---------------------------------------------------------\n",
            "0 | backbone          | RegNet             | 4.0 M \n",
            "1 | domain_classifier | Sequential         | 48.8 K\n",
            "2 | train_accuracy    | MulticlassAccuracy | 0     \n",
            "3 | val_accuracy      | MulticlassAccuracy | 0     \n",
            "4 | test_accuracy     | MulticlassAccuracy | 0     \n",
            "---------------------------------------------------------\n",
            "4.0 M     Trainable params\n",
            "0         Non-trainable params\n",
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def _train_loop():\n",
        "    seed_everything(SEED, workers=True)\n",
        "\n",
        "    print(MODEL_NAME)\n",
        "    model = MODEL()\n",
        "\n",
        "    callbacks = list()\n",
        "\n",
        "    checkpoint = ModelCheckpoint(\n",
        "        monitor=METRIC_TO_MONITOR,\n",
        "        dirpath=f\"{EXPERIMENT_DIR}/model\",\n",
        "        mode=METRIC_MODE,\n",
        "        filename=f\"{MODEL_NAME}_best\",\n",
        "    )\n",
        "    callbacks.append(checkpoint)\n",
        "\n",
        "    early_stopping = EarlyStopping(\n",
        "        monitor=METRIC_TO_MONITOR,\n",
        "        min_delta=0.00,\n",
        "        patience=int(\n",
        "            EARLY_STOPPING_PATIENCE * EPOCH\n",
        "        ),\n",
        "        verbose=False,\n",
        "        mode=METRIC_MODE,\n",
        "    )\n",
        "    callbacks.append(early_stopping)\n",
        "\n",
        "    if os.path.exists(BEST_MODEL_PATH):\n",
        "        ckpt_path = BEST_MODEL_PATH\n",
        "    else:\n",
        "        ckpt_path = None\n",
        "\n",
        "    trainer = Trainer(\n",
        "        accelerator=\"auto\",\n",
        "        devices=1,\n",
        "        max_epochs=EPOCH,\n",
        "        logger=False,\n",
        "        callbacks=callbacks,\n",
        "        log_every_n_steps=5,\n",
        "    )\n",
        "    trainer.fit(model, ckpt_path=ckpt_path)\n",
        "\n",
        "_train_loop()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6MmmuwWlqsU"
      },
      "source": [
        "## **Testing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "1qq1LddKlqva",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388,
          "referenced_widgets": [
            "1ca126e7d51343608548fc281155b299",
            "cdbc1b98589445608e55f0ecf9e5716a",
            "cee231e6f0a6408294c53be0ff8eee99",
            "b9b0e4428ad94a4b9817d32595902f3b",
            "31616c118c3d44f3babda5cbe27c5240",
            "dddb4fee7cf540a1a0a455cdfe4355d5",
            "3b6cff9e47344e61aa2b934bece203c8",
            "19074d732ced40568db716c565f06202",
            "6b1545c8211a4d82b8a44d55ff9f7f38",
            "7033d131f771483485a791ff0fe8e168",
            "a5af624fc78d4d11a4208fcff965ac56"
          ]
        },
        "outputId": "c7559a26-5ef8-4352-e9b3-623a4818f442"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: GPU available: True (cuda), used: True\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO: Restoring states from the checkpoint path at experiment/model/DANN_best.ckpt\n",
            "INFO:lightning.pytorch.utilities.rank_zero:Restoring states from the checkpoint path at experiment/model/DANN_best.ckpt\n",
            "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO: Loaded model weights from the checkpoint at experiment/model/DANN_best.ckpt\n",
            "INFO:lightning.pytorch.utilities.rank_zero:Loaded model weights from the checkpoint at experiment/model/DANN_best.ckpt\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ca126e7d51343608548fc281155b299"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.4478934109210968    \u001b[0m\u001b[35m \u001b[0m│\n",
              "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    3.137967824935913    \u001b[0m\u001b[35m \u001b[0m│\n",
              "└───────────────────────────┴───────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.4478934109210968     </span>│\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     3.137967824935913     </span>│\n",
              "└───────────────────────────┴───────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def _test_loop():\n",
        "    trainer = Trainer(accelerator='auto', logger=False)\n",
        "    model = MODEL()\n",
        "    trainer.test(model=model, ckpt_path=BEST_MODEL_PATH)\n",
        "\n",
        "_test_loop()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FjnVa4jlqyb"
      },
      "source": [
        "## **Inference**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "xd_kCUUAlq14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "outputId": "37c04b3b-b430-4b86-f82d-6444b742da4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using downloaded and verified file: experiment/dataset/test_32x32.mat\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = MODEL.load_from_checkpoint(\n",
        "    BEST_MODEL_PATH\n",
        ")\n",
        "model.eval()\n",
        "\n",
        "plt.clf()\n",
        "fig = plt.figure()\n",
        "plt.subplots_adjust(\n",
        "    left=0.1,\n",
        "    bottom=0.1,\n",
        "    right=math.sqrt(2),\n",
        "    top=math.sqrt(3),\n",
        "    wspace=0.4,\n",
        "    hspace=0.4,\n",
        ")\n",
        "\n",
        "class_bin = list()\n",
        "\n",
        "InferenceDataset = SVHN(\n",
        "    root='experiment/dataset/',\n",
        "    split=\"test\",\n",
        "    download=True,\n",
        ")\n",
        "\n",
        "for index in range(9):\n",
        "    while True:\n",
        "        sample_idx = random.randint(0, len(InferenceDataset) - 1)\n",
        "        image, gt_class = InferenceDataset[sample_idx]\n",
        "\n",
        "        if not gt_class in class_bin:\n",
        "            class_bin.append(gt_class)\n",
        "            break\n",
        "        else:\n",
        "            continue\n",
        "\n",
        "    prediction = model(\n",
        "        TRANSFORM(image).to(\n",
        "            \"cuda\" if torch.cuda.is_available else \"cpu\"\n",
        "        ).unsqueeze(0),\n",
        "    )\n",
        "\n",
        "    prediction.softmax(1)\n",
        "    predicted_class = torch.argmax(prediction).data.cpu().numpy()\n",
        "\n",
        "    title = f\"Predicted: {predicted_class}\\n\"\n",
        "    title += f\"Ground-truth: {gt_class}\"\n",
        "\n",
        "    ax = fig.add_subplot(3, 3, index + 1)\n",
        "\n",
        "    ax.imshow(np.array(image).astype(np.uint8))\n",
        "    ax.set_title(title, fontsize=8)\n",
        "    ax.set_axis_off()\n",
        "\n",
        "plt.show()\n",
        "\n",
        "del InferenceDataset"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "bn6rOS6Yls4f",
        "vKzqAlqRmjJB",
        "8KYLw9Avltiv",
        "0UGg5It9lt-1",
        "vGe2laAvluJs",
        "DfSTO7GrqL-E",
        "v72HiXPUlqMX",
        "FMljZV18VuJB",
        "Ow0UIr5wlqV0",
        "8vxG6Uzflqjl",
        "o6MmmuwWlqsU",
        "1FjnVa4jlqyb"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "5c26b44eac084dd296affab11feba9bf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
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            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
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