testingCNNmodels.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "testingCNNmodels.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aE_n-CoyFgj_",
        "colab_type": "text"
      },
      "source": [
        "Testing both the custom built ResNet9 and ResNet15"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekZHJ2hz02An",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "a44728cb-57cd-4191-a4f3-4d6c3f359d43"
      },
      "source": [
        "#Importing libraries\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rT7LPviX1Vjf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "13cc47d0-d20b-4d4b-b93f-ed812840ce1b"
      },
      "source": [
        "!pip install import_ipynb\n",
        "import import_ipynb"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting import_ipynb\n",
            "  Downloading https://files.pythonhosted.org/packages/63/35/495e0021bfdcc924c7cdec4e9fbb87c88dd03b9b9b22419444dc370c8a45/import-ipynb-0.1.3.tar.gz\n",
            "Building wheels for collected packages: import-ipynb\n",
            "  Building wheel for import-ipynb (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for import-ipynb: filename=import_ipynb-0.1.3-cp36-none-any.whl size=2976 sha256=fd14f250ef23f4ca18b073df946cb5f6d0772d59424f2d0fb5f74412673e241b\n",
            "  Stored in directory: /root/.cache/pip/wheels/b4/7b/e9/a3a6e496115dffdb4e3085d0ae39ffe8a814eacc44bbf494b5\n",
            "Successfully built import-ipynb\n",
            "Installing collected packages: import-ipynb\n",
            "Successfully installed import-ipynb-0.1.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eN4tayU_0_Vm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b3ea6cd8-02d8-4deb-b576-a8e1836cf030"
      },
      "source": [
        "%cd '/content/gdrive/My Drive/Colab Notebooks/Face mask'"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/Colab Notebooks/Face mask\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EG9fFAf51MJu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ad05f218-3e06-49bf-fcac-4862dd7c7039"
      },
      "source": [
        "#importing modules\n",
        "from CNN import ResNet9, ResNet15\n",
        "from DataPreprocessing import Preprocessing"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "importing Jupyter notebook from CNN.ipynb\n",
            "importing Jupyter notebook from DataPreprocessing.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mpo7k2Zv1jLc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device('cuda')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlSWlDR20yBB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7cc4b190-ddbb-4dd3-bc0e-ecffb1a328f7"
      },
      "source": [
        "#model and loading the state_dict\n",
        "model15 = ResNet15(1,2).to(device)\n",
        "model9 = ResNet9(1,2).to(device)\n",
        "model15.load_state_dict(torch.load('state_dict_resnet15.pth'))\n",
        "model9.load_state_dict(torch.load('state_dict_resnet9.pth'))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7UoC0s12azM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = Preprocessing(path={'test':'dataset/test',\n",
        "                          'train':'dataset/train'})\n",
        "_, test_ds = data.preprocessed_dataset()\n",
        "test_dl = DataLoader(test_ds, 10, num_workers=3,\n",
        "                      pin_memory=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGmtIgV62MmX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "e714dac9-9f07-4843-a96f-46744fe6b907"
      },
      "source": [
        "#testing\n",
        "accuracy9 = []\n",
        "accuracy15 = []\n",
        "for x, y in test_dl:\n",
        "    x, y = x.to(device), y.to(device)\n",
        "    _, label15 = torch.max(torch.exp(model15(x)), dim=1)\n",
        "    _, label9 = torch.max(torch.exp(model9(x)), dim=1)\n",
        "    acc9 = torch.sum(label9 == y).item()/len(y)\n",
        "    acc15 = torch.sum(label15 == y).item()/len(y)\n",
        "    accuracy9.append(acc9)\n",
        "    accuracy15.append(acc15)\n",
        "    print('ResNet9: {}, ResNet15: {}'.format(acc9, acc15))\n",
        "    "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:100: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ResNet9: 0.8, ResNet15: 1.0\n",
            "ResNet9: 0.8, ResNet15: 1.0\n",
            "ResNet9: 1.0, ResNet15: 1.0\n",
            "ResNet9: 0.9, ResNet15: 1.0\n",
            "ResNet9: 0.9, ResNet15: 0.9\n",
            "ResNet9: 1.0, ResNet15: 0.7\n",
            "ResNet9: 0.8, ResNet15: 0.9\n",
            "ResNet9: 1.0, ResNet15: 0.9\n",
            "ResNet9: 0.9, ResNet15: 0.8\n",
            "ResNet9: 0.8888888888888888, ResNet15: 0.7777777777777778\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMnJcrGZ3sPC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "3e2774d7-291e-4e95-9feb-0b4cddcdaa38"
      },
      "source": [
        "plt.plot(accuracy9, 'r--x', markersize=8, markeredgewidth=3\n",
        "         , linewidth=3,label='ResNet9')\n",
        "plt.plot(accuracy15, 'c-1', markersize=12, markeredgewidth=3\n",
        "         , linewidth=2,label='ResNet15')\n",
        "plt.grid()\n",
        "plt.title('Accuracy: ResNet9 vs ResNet15')\n",
        "plt.xlabel('batches')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd3b50dab00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}