Alexnet.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/Alexnet.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": "208d79a0-addb-4a03-d42f-e4c37065fbc4"
      },
      "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.alexnet(pretrained=True).to(device)\n",
        "    model.eval()"
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        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-4df8aa71.pth\n"
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            "\n"
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              "AlexNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
              "  (classifier): Sequential(\n",
              "    (0): Dropout(p=0.5, inplace=False)\n",
              "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): Dropout(p=0.5, inplace=False)\n",
              "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
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          },
          "execution_count": 5
        }
      ]
    },
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      "metadata": {
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        "outputId": "b47a3b6e-db8e-4fba-bac6-f03e41e2b24f"
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      "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))"
      ],
      "execution_count": null,
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        {
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            "step : 1 / 500.0\n",
            "top-1 percentage :  57.00%\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUnahgricKZN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}