Curt-Park/knn_with_gpu.ipynb

## knn_with_gpu.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPu5DH3CpO9KMI834FKbRf7",
      "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/gist/Curt-Park/3cea773e533ba8369a73e5b67c53b062/knn_with_gpu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN with GPU\n",
        "\n",
        "시작하기 전:\n",
        "\n",
        "Colab의 상단에서, `수정` -> `노트설정` -> `T4 GPU` -> `저장`"
      ],
      "metadata": {
        "id": "xEDkHggzDFto"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmtHQqmlxtvI",
        "outputId": "86242584-00c6-43ff-e36c-950bf76c7f64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting cifar10_web\n",
            "  Downloading cifar10_web-0.0.4-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: cifar10_web\n",
            "Successfully installed cifar10_web-0.0.4\n"
          ]
        }
      ],
      "source": [
        "!pip install cifar10_web"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import cupy as cp  # for GPU acceleration.\n",
        "import matplotlib.pyplot as plt  # for visualization.\n",
        "\n",
        "from cifar10_web import cifar10"
      ],
      "metadata": {
        "id": "qWWVMPU7-_Ii"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the dataset.\n",
        "train_images, train_labels, test_images, test_labels = cifar10(path=\".\")\n",
        "\n",
        "# One-hot to idx.\n",
        "train_labels = np.argmax(train_labels, axis=1)\n",
        "test_labels = np.argmax(test_labels, axis=1)\n",
        "\n",
        "# Check the dataset size.\n",
        "print(\"train image shape:\", train_images.shape)\n",
        "print(\"train lable shape:\", train_labels.shape)\n",
        "print(\"test image shape:\", test_images.shape)\n",
        "print(\"test lable shape:\", test_labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gn3nPX8W-6dQ",
        "outputId": "64ba0c49-ccd6-423b-f00a-3f07c8aadd00"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train image shape: (50000, 3072)\n",
            "train lable shape: (50000,)\n",
            "test image shape: (10000, 3072)\n",
            "test lable shape: (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class KNearestNeighbor:\n",
        "  \"\"\"KNN.\"\"\"\n",
        "\n",
        "  def __init__(self, x: np.ndarray, y: np.ndarray, k: int) -> None:\n",
        "    \"\"\"Initialize the dataset and parameter k.\"\"\"\n",
        "    self.x = cp.array(x)\n",
        "    self.y = cp.array(y)\n",
        "    self.k = k\n",
        "\n",
        "  def train(self, x: np.ndarray, y: np.ndarray) -> None:\n",
        "    \"\"\"x is N x D where each row is an example and y is 1-dimension of size N.\"\"\"\n",
        "    self.x = cp.array(x)\n",
        "    self.y = cp.array(y)\n",
        "\n",
        "  def predict(self, x: np.ndarray) -> int:\n",
        "    \"\"\"x is 1 x D where each row is an example we wish to predict label for.\"\"\"\n",
        "    # to GPU.\n",
        "    x = cp.expand_dims(cp.array(x), axis=0)\n",
        "\n",
        "    # L2 distances shaped in (N,).\n",
        "    distances = cp.sum((self.x - x) ** 2, axis=-1)\n",
        "\n",
        "    # get the indexs from the smallest distance. (k,)\n",
        "    min_k_idxs = cp.argpartition(distances, kth=self.k)[:self.k]\n",
        "\n",
        "    # get the predictions.\n",
        "    y_pred = self.y[min_k_idxs]\n",
        "    prediction = cp.argmax(cp.bincount(y_pred))\n",
        "\n",
        "    return int(prediction)"
      ],
      "metadata": {
        "id": "j3fyXZ9dyq3Y"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a knn instance.\n",
        "knn = KNearestNeighbor(x=train_images, y=train_labels, k=3)"
      ],
      "metadata": {
        "id": "E8dZjUBd12vE"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Choice N samples for testing.\n",
        "idxs = np.random.choice(len(test_images), 20)\n",
        "x_test = test_images[idxs]\n",
        "y_test = test_labels[idxs]\n",
        "print(x_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jlBbstl2Y1t",
        "outputId": "94fca436-1065-42d7-f8aa-6ffe271fae7c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20, 3072) (20,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run predictions.\n",
        "predictions = []\n",
        "for i in range(len(x_test)):\n",
        "  predictions.append(knn.predict(x_test[i]))\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MfHFdhz6eij",
        "outputId": "3f27de57-a638-4af5-91c0-3e1ff27ef5ab"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 6, 0, 2, 2, 0, 4, 8, 6, 2, 2, 4, 5, 7, 2, 0, 6, 0, 0, 4]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the accuracy.\n",
        "print(\"Accuracy: \", sum(predictions == y_test) / len(predictions) * 100, \"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFc5pfsj_Os4",
        "outputId": "aabfab7f-e2fe-432e-d788-0a16bd53a618"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  45.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rotated_images = np.array(list(map(lambda i: x_test[i].reshape(3,32,32).transpose(1,2,0), range(len(x_test)))))\n",
        "labels_mapping = [ \"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\" ]"
      ],
      "metadata": {
        "id": "XYZrFyFpE8H2"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(0)\n",
        "fig.set_size_inches(30, 30)\n",
        "for i in range(len(x_test)):\n",
        "    fig.add_subplot(5, 5, i+1)\n",
        "    plt.imshow(rotated_images[i])\n",
        "    plt.title(f\"Label: {labels_mapping[y_test[i]]} / Prediction: {labels_mapping[predictions[i]]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BBylcnjWGXgl",
        "outputId": "30aa6cd0-812d-4809-e143-0f73b00984fa"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x3000 with 20 Axes>"
            ],
            "image/png": 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    }
  ]
}