GoogLeNet.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeremy0405/assignment2/blob/main/GoogLeNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XEmdwwFIbh_B",
        "outputId": "4e94d0ce-605c-4481-f364-cb24f799da68"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pi34Kj-Gb2zW"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.utils.data as data\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsU4F_lFb23H"
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      "source": [
        "if __name__ == \"__main__\":\n",
        "    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
        "                                     std=[0.229, 0.224, 0.225])\n",
        "\n",
        "    transform = transforms.Compose(\n",
        "        [transforms.Resize(256),\n",
        "         transforms.CenterCrop(224),\n",
        "         transforms.ToTensor(),\n",
        "         normalize,\n",
        "         ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GLuOGgib256"
      },
      "source": [
        "    test_set = torchvision.datasets.ImageFolder(root=\"/content/drive/My Drive/ILSVRC2012_img_val/\", transform=transform)\n",
        "    test_loader = data.DataLoader(test_set, batch_size=100, shuffle=True, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "    model = torchvision.models.googlenet(pretrained=True).to(device)\n",
        "    model.eval()"
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          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/googlenet-1378be20.pth\" to /root/.cache/torch/hub/checkpoints/googlenet-1378be20.pth\n"
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              "GoogLeNet(\n",
              "  (conv1): BasicConv2d(\n",
              "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (conv2): BasicConv2d(\n",
              "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (conv3): BasicConv2d(\n",
              "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception3a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception3b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception4a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4c): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4d): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4e): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception5a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception5b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (aux1): None\n",
              "  (aux2): None\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              "  (fc): Linear(in_features=1024, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vk0DyqHfcKTY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55f91447-92e7-426b-b627-d11aef936ff4"
      },
      "source": [
        "    correct_top1 = 0\n",
        "    total = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for idx, (images, labels) in enumerate(test_loader):\n",
        "\n",
        "            images = images.to(device)      # [100, 3, 224, 224]\n",
        "            labels = labels.to(device)      # [100]\n",
        "            outputs = model(images)\n",
        "\n",
        "            _, pred = torch.max(outputs, 1)\n",
        "            total += labels.size(0)\n",
        "            correct_top1 += (pred == labels).sum().item()\n",
        "\n",
        "            print(\"step : {} / {}\".format(idx + 1, len(test_set)/int(labels.size(0))))\n",
        "            print(\"top-1 percentage :  {0:0.2f}%\".format(correct_top1 / total * 100))\n",
        "            \n",
        "    print(\"top-1 percentage :  {0:0.2f}%\".format(correct_top1 / total * 100))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "step : 1 / 500.0\n",
            "top-1 percentage :  67.00%\n",
            "step : 2 / 500.0\n",
            "top-1 percentage :  69.00%\n",
            "step : 3 / 500.0\n",
            "top-1 percentage :  68.33%\n",
            "step : 4 / 500.0\n",
            "top-1 percentage :  67.75%\n",
            "step : 5 / 500.0\n",
            "top-1 percentage :  67.80%\n",
            "step : 6 / 500.0\n",
            "top-1 percentage :  68.00%\n",
            "step : 7 / 500.0\n",
            "top-1 percentage :  68.71%\n",
            "step : 8 / 500.0\n",
            "top-1 percentage :  68.62%\n",
            "step : 9 / 500.0\n",
            "top-1 percentage :  69.11%\n",
            "step : 10 / 500.0\n",
            "top-1 percentage :  69.90%\n",
            "step : 11 / 500.0\n",
            "top-1 percentage :  70.00%\n",
            "step : 12 / 500.0\n",
            "top-1 percentage :  70.17%\n",
            "step : 13 / 500.0\n",
            "top-1 percentage :  70.23%\n",
            "step : 14 / 500.0\n",
            "top-1 percentage :  69.93%\n",
            "step : 15 / 500.0\n",
            "top-1 percentage :  69.67%\n",
            "step : 16 / 500.0\n",
            "top-1 percentage :  69.94%\n",
            "step : 17 / 500.0\n",
            "top-1 percentage :  69.88%\n",
            "step : 18 / 500.0\n",
            "top-1 percentage :  69.94%\n",
            "step : 19 / 500.0\n",
            "top-1 percentage :  70.11%\n",
            "step : 20 / 500.0\n",
            "top-1 percentage :  70.00%\n",
            "step : 21 / 500.0\n",
            "top-1 percentage :  69.52%\n",
            "step : 22 / 500.0\n",
            "top-1 percentage :  69.59%\n",
            "step : 23 / 500.0\n",
            "top-1 percentage :  69.48%\n",
            "step : 24 / 500.0\n",
            "top-1 percentage :  69.58%\n",
            "step : 25 / 500.0\n",
            "top-1 percentage :  69.60%\n",
            "step : 26 / 500.0\n",
            "top-1 percentage :  69.69%\n",
            "step : 27 / 500.0\n",
            "top-1 percentage :  69.70%\n",
            "step : 28 / 500.0\n",
            "top-1 percentage :  69.89%\n",
            "step : 29 / 500.0\n",
            "top-1 percentage :  69.93%\n",
            "step : 30 / 500.0\n",
            "top-1 percentage :  69.97%\n",
            "step : 31 / 500.0\n",
            "top-1 percentage :  69.77%\n",
            "step : 32 / 500.0\n",
            "top-1 percentage :  69.72%\n",
            "step : 33 / 500.0\n",
            "top-1 percentage :  70.00%\n",
            "step : 34 / 500.0\n",
            "top-1 percentage :  70.09%\n",
            "step : 35 / 500.0\n",
            "top-1 percentage :  70.03%\n",
            "step : 36 / 500.0\n",
            "top-1 percentage :  70.08%\n",
            "step : 37 / 500.0\n",
            "top-1 percentage :  70.08%\n",
            "step : 38 / 500.0\n",
            "top-1 percentage :  70.26%\n",
            "step : 39 / 500.0\n",
            "top-1 percentage :  70.21%\n",
            "step : 40 / 500.0\n",
            "top-1 percentage :  70.03%\n",
            "step : 41 / 500.0\n",
            "top-1 percentage :  70.07%\n",
            "step : 42 / 500.0\n",
            "top-1 percentage :  70.21%\n",
            "step : 43 / 500.0\n",
            "top-1 percentage :  70.30%\n",
            "step : 44 / 500.0\n",
            "top-1 percentage :  70.30%\n",
            "step : 45 / 500.0\n",
            "top-1 percentage :  70.49%\n",
            "step : 46 / 500.0\n",
            "top-1 percentage :  70.57%\n",
            "step : 47 / 500.0\n",
            "top-1 percentage :  70.53%\n",
            "step : 48 / 500.0\n",
            "top-1 percentage :  70.58%\n",
            "step : 49 / 500.0\n",
            "top-1 percentage :  70.76%\n",
            "step : 50 / 500.0\n",
            "top-1 percentage :  70.74%\n",
            "step : 51 / 500.0\n",
            "top-1 percentage :  70.65%\n",
            "step : 52 / 500.0\n",
            "top-1 percentage :  70.54%\n",
            "step : 53 / 500.0\n",
            "top-1 percentage :  70.49%\n",
            "step : 54 / 500.0\n",
            "top-1 percentage :  70.44%\n",
            "step : 55 / 500.0\n",
            "top-1 percentage :  70.40%\n",
            "step : 56 / 500.0\n",
            "top-1 percentage :  70.43%\n",
            "step : 57 / 500.0\n",
            "top-1 percentage :  70.44%\n",
            "step : 58 / 500.0\n",
            "top-1 percentage :  70.29%\n",
            "step : 59 / 500.0\n",
            "top-1 percentage :  70.20%\n",
            "step : 60 / 500.0\n",
            "top-1 percentage :  70.13%\n",
            "step : 61 / 500.0\n",
            "top-1 percentage :  70.18%\n",
            "step : 62 / 500.0\n",
            "top-1 percentage :  70.19%\n",
            "step : 63 / 500.0\n",
            "top-1 percentage :  70.17%\n",
            "step : 64 / 500.0\n",
            "top-1 percentage :  70.19%\n",
            "step : 65 / 500.0\n",
            "top-1 percentage :  70.18%\n",
            "step : 66 / 500.0\n",
            "top-1 percentage :  70.09%\n",
            "step : 67 / 500.0\n",
            "top-1 percentage :  70.09%\n",
            "step : 68 / 500.0\n",
            "top-1 percentage :  70.07%\n",
            "step : 69 / 500.0\n",
            "top-1 percentage :  70.04%\n",
            "step : 70 / 500.0\n",
            "top-1 percentage :  70.04%\n",
            "step : 71 / 500.0\n",
            "top-1 percentage :  70.00%\n",
            "step : 72 / 500.0\n",
            "top-1 percentage :  69.93%\n",
            "step : 73 / 500.0\n",
            "top-1 percentage :  69.97%\n",
            "step : 74 / 500.0\n",
            "top-1 percentage :  69.89%\n",
            "step : 75 / 500.0\n",
            "top-1 percentage :  69.88%\n",
            "step : 76 / 500.0\n",
            "top-1 percentage :  69.86%\n",
            "step : 77 / 500.0\n",
            "top-1 percentage :  69.79%\n",
            "step : 78 / 500.0\n",
            "top-1 percentage :  69.72%\n",
            "step : 79 / 500.0\n",
            "top-1 percentage :  69.68%\n",
            "step : 80 / 500.0\n",
            "top-1 percentage :  69.66%\n",
            "step : 81 / 500.0\n",
            "top-1 percentage :  69.73%\n",
            "step : 82 / 500.0\n",
            "top-1 percentage :  69.71%\n",
            "step : 83 / 500.0\n",
            "top-1 percentage :  69.70%\n",
            "step : 84 / 500.0\n",
            "top-1 percentage :  69.69%\n",
            "step : 85 / 500.0\n",
            "top-1 percentage :  69.71%\n",
            "step : 86 / 500.0\n",
            "top-1 percentage :  69.79%\n",
            "step : 87 / 500.0\n",
            "top-1 percentage :  69.80%\n",
            "step : 88 / 500.0\n",
            "top-1 percentage :  69.78%\n",
            "step : 89 / 500.0\n",
            "top-1 percentage :  69.73%\n",
            "step : 90 / 500.0\n",
            "top-1 percentage :  69.79%\n",
            "step : 91 / 500.0\n",
            "top-1 percentage :  69.76%\n",
            "step : 92 / 500.0\n",
            "top-1 percentage :  69.71%\n",
            "step : 93 / 500.0\n",
            "top-1 percentage :  69.71%\n",
            "step : 94 / 500.0\n",
            "top-1 percentage :  69.65%\n",
            "step : 95 / 500.0\n",
            "top-1 percentage :  69.63%\n",
            "step : 96 / 500.0\n",
            "top-1 percentage :  69.60%\n",
            "step : 97 / 500.0\n",
            "top-1 percentage :  69.70%\n",
            "step : 98 / 500.0\n",
            "top-1 percentage :  69.69%\n",
            "step : 99 / 500.0\n",
            "top-1 percentage :  69.62%\n",
            "step : 100 / 500.0\n",
            "top-1 percentage :  69.63%\n",
            "step : 101 / 500.0\n",
            "top-1 percentage :  69.70%\n",
            "step : 102 / 500.0\n",
            "top-1 percentage :  69.62%\n",
            "step : 103 / 500.0\n",
            "top-1 percentage :  69.59%\n",
            "step : 104 / 500.0\n",
            "top-1 percentage :  69.63%\n",
            "step : 105 / 500.0\n",
            "top-1 percentage :  69.65%\n",
            "step : 106 / 500.0\n",
            "top-1 percentage :  69.65%\n",
            "step : 107 / 500.0\n",
            "top-1 percentage :  69.62%\n",
            "step : 108 / 500.0\n",
            "top-1 percentage :  69.64%\n",
            "step : 109 / 500.0\n",
            "top-1 percentage :  69.64%\n",
            "step : 110 / 500.0\n",
            "top-1 percentage :  69.64%\n",
            "step : 111 / 500.0\n",
            "top-1 percentage :  69.68%\n",
            "step : 112 / 500.0\n",
            "top-1 percentage :  69.65%\n",
            "step : 113 / 500.0\n",
            "top-1 percentage :  69.60%\n",
            "step : 114 / 500.0\n",
            "top-1 percentage :  69.64%\n",
            "step : 115 / 500.0\n",
            "top-1 percentage :  69.63%\n",
            "step : 116 / 500.0\n",
            "top-1 percentage :  69.60%\n",
            "step : 117 / 500.0\n",
            "top-1 percentage :  69.60%\n",
            "step : 118 / 500.0\n",
            "top-1 percentage :  69.59%\n",
            "step : 119 / 500.0\n",
            "top-1 percentage :  69.66%\n",
            "step : 120 / 500.0\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUnahgricKZN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}