Pruning_quantiztion.ipynb

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        "<a href=\"https://colab.research.google.com/github/Gedeon-m-gedus/Image_Processing/blob/master/Pruning_quantiztion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "metadata": {
        "id": "6wwgzt-5lkru",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install torchsummaryX"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtmCPCbfCsJ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import numpy as np\n",
        "import torch.nn.utils.prune as prune\n",
        "import torchvision.models as models\n",
        "import torch.nn.functional as F\n",
        "from torchsummaryX import summary\n",
        "from torchvision import datasets, transforms as T\n",
        "import os"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jG6uXrX7FnSJ",
        "colab_type": "code",
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      "source": [
        "def set_seed(seed):\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "def handle_dirs(dirpath):\n",
        "    if not os.path.exists(dirpath):\n",
        "        os.makedirs(dirpath)\n",
        "\n",
        "set_seed(42)"
      ],
      "execution_count": 9,
      "outputs": []
    },
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        "outputId": "ffff82d8-d8df-4b95-bdba-dc7a6a592277"
      },
      "source": [
        "# Colab's file access feature\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 12,
      "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"
        }
      ]
    },
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      "metadata": {
        "id": "mkBARficK1yG",
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        "save_dir = \"/content/gdrive/My Drive/AMMI_project/object_detector\"\n",
        "handle_dirs(save_dir)"
      ],
      "execution_count": 14,
      "outputs": []
    },
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        "model = models.resnext50_32x4d(pretrained=True)"
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      "outputs": [
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          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth\" to /root/.cache/torch/checkpoints/resnext50_32x4d-7cdf4587.pth\n"
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      "metadata": {
        "id": "m--zjI54DuLB",
        "colab_type": "code",
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      "source": [
        "model.state_dict();\n",
        "PATH = save_dir + '/Resnet50_full_precission.pth'\n",
        "torch.save(model.state_dict(), PATH)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
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        "id": "yV-w1r5ND2wO",
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            "====================================================================================================\n",
            "                                             Kernel Shape       Output Shape  \\\n",
            "Layer                                                                          \n",
            "0_conv1                                     [3, 64, 7, 7]  [1, 64, 112, 112]   \n",
            "1_bn1                                                [64]  [1, 64, 112, 112]   \n",
            "2_relu                                                  -  [1, 64, 112, 112]   \n",
            "3_maxpool                                               -    [1, 64, 56, 56]   \n",
            "4_layer1.0.Conv2d_conv1                   [64, 128, 1, 1]   [1, 128, 56, 56]   \n",
            "5_layer1.0.BatchNorm2d_bn1                          [128]   [1, 128, 56, 56]   \n",
            "6_layer1.0.ReLU_relu                                    -   [1, 128, 56, 56]   \n",
            "7_layer1.0.Conv2d_conv2                    [4, 128, 3, 3]   [1, 128, 56, 56]   \n",
            "8_layer1.0.BatchNorm2d_bn2                          [128]   [1, 128, 56, 56]   \n",
            "9_layer1.0.ReLU_relu                                    -   [1, 128, 56, 56]   \n",
            "10_layer1.0.Conv2d_conv3                 [128, 256, 1, 1]   [1, 256, 56, 56]   \n",
            "11_layer1.0.BatchNorm2d_bn3                         [256]   [1, 256, 56, 56]   \n",
            "12_layer1.0.downsample.Conv2d_0           [64, 256, 1, 1]   [1, 256, 56, 56]   \n",
            "13_layer1.0.downsample.BatchNorm2d_1                [256]   [1, 256, 56, 56]   \n",
            "14_layer1.0.ReLU_relu                                   -   [1, 256, 56, 56]   \n",
            "15_layer1.1.Conv2d_conv1                 [256, 128, 1, 1]   [1, 128, 56, 56]   \n",
            "16_layer1.1.BatchNorm2d_bn1                         [128]   [1, 128, 56, 56]   \n",
            "17_layer1.1.ReLU_relu                                   -   [1, 128, 56, 56]   \n",
            "18_layer1.1.Conv2d_conv2                   [4, 128, 3, 3]   [1, 128, 56, 56]   \n",
            "19_layer1.1.BatchNorm2d_bn2                         [128]   [1, 128, 56, 56]   \n",
            "20_layer1.1.ReLU_relu                                   -   [1, 128, 56, 56]   \n",
            "21_layer1.1.Conv2d_conv3                 [128, 256, 1, 1]   [1, 256, 56, 56]   \n",
            "22_layer1.1.BatchNorm2d_bn3                         [256]   [1, 256, 56, 56]   \n",
            "23_layer1.1.ReLU_relu                                   -   [1, 256, 56, 56]   \n",
            "24_layer1.2.Conv2d_conv1                 [256, 128, 1, 1]   [1, 128, 56, 56]   \n",
            "25_layer1.2.BatchNorm2d_bn1                         [128]   [1, 128, 56, 56]   \n",
            "26_layer1.2.ReLU_relu                                   -   [1, 128, 56, 56]   \n",
            "27_layer1.2.Conv2d_conv2                   [4, 128, 3, 3]   [1, 128, 56, 56]   \n",
            "28_layer1.2.BatchNorm2d_bn2                         [128]   [1, 128, 56, 56]   \n",
            "29_layer1.2.ReLU_relu                                   -   [1, 128, 56, 56]   \n",
            "30_layer1.2.Conv2d_conv3                 [128, 256, 1, 1]   [1, 256, 56, 56]   \n",
            "31_layer1.2.BatchNorm2d_bn3                         [256]   [1, 256, 56, 56]   \n",
            "32_layer1.2.ReLU_relu                                   -   [1, 256, 56, 56]   \n",
            "33_layer2.0.Conv2d_conv1                 [256, 256, 1, 1]   [1, 256, 56, 56]   \n",
            "34_layer2.0.BatchNorm2d_bn1                         [256]   [1, 256, 56, 56]   \n",
            "35_layer2.0.ReLU_relu                                   -   [1, 256, 56, 56]   \n",
            "36_layer2.0.Conv2d_conv2                   [8, 256, 3, 3]   [1, 256, 28, 28]   \n",
            "37_layer2.0.BatchNorm2d_bn2                         [256]   [1, 256, 28, 28]   \n",
            "38_layer2.0.ReLU_relu                                   -   [1, 256, 28, 28]   \n",
            "39_layer2.0.Conv2d_conv3                 [256, 512, 1, 1]   [1, 512, 28, 28]   \n",
            "40_layer2.0.BatchNorm2d_bn3                         [512]   [1, 512, 28, 28]   \n",
            "41_layer2.0.downsample.Conv2d_0          [256, 512, 1, 1]   [1, 512, 28, 28]   \n",
            "42_layer2.0.downsample.BatchNorm2d_1                [512]   [1, 512, 28, 28]   \n",
            "43_layer2.0.ReLU_relu                                   -   [1, 512, 28, 28]   \n",
            "44_layer2.1.Conv2d_conv1                 [512, 256, 1, 1]   [1, 256, 28, 28]   \n",
            "45_layer2.1.BatchNorm2d_bn1                         [256]   [1, 256, 28, 28]   \n",
            "46_layer2.1.ReLU_relu                                   -   [1, 256, 28, 28]   \n",
            "47_layer2.1.Conv2d_conv2                   [8, 256, 3, 3]   [1, 256, 28, 28]   \n",
            "48_layer2.1.BatchNorm2d_bn2                         [256]   [1, 256, 28, 28]   \n",
            "49_layer2.1.ReLU_relu                                   -   [1, 256, 28, 28]   \n",
            "50_layer2.1.Conv2d_conv3                 [256, 512, 1, 1]   [1, 512, 28, 28]   \n",
            "51_layer2.1.BatchNorm2d_bn3                         [512]   [1, 512, 28, 28]   \n",
            "52_layer2.1.ReLU_relu                                   -   [1, 512, 28, 28]   \n",
            "53_layer2.2.Conv2d_conv1                 [512, 256, 1, 1]   [1, 256, 28, 28]   \n",
            "54_layer2.2.BatchNorm2d_bn1                         [256]   [1, 256, 28, 28]   \n",
            "55_layer2.2.ReLU_relu                                   -   [1, 256, 28, 28]   \n",
            "56_layer2.2.Conv2d_conv2                   [8, 256, 3, 3]   [1, 256, 28, 28]   \n",
            "57_layer2.2.BatchNorm2d_bn2                         [256]   [1, 256, 28, 28]   \n",
            "58_layer2.2.ReLU_relu                                   -   [1, 256, 28, 28]   \n",
            "59_layer2.2.Conv2d_conv3                 [256, 512, 1, 1]   [1, 512, 28, 28]   \n",
            "60_layer2.2.BatchNorm2d_bn3                         [512]   [1, 512, 28, 28]   \n",
            "61_layer2.2.ReLU_relu                                   -   [1, 512, 28, 28]   \n",
            "62_layer2.3.Conv2d_conv1                 [512, 256, 1, 1]   [1, 256, 28, 28]   \n",
            "63_layer2.3.BatchNorm2d_bn1                         [256]   [1, 256, 28, 28]   \n",
            "64_layer2.3.ReLU_relu                                   -   [1, 256, 28, 28]   \n",
            "65_layer2.3.Conv2d_conv2                   [8, 256, 3, 3]   [1, 256, 28, 28]   \n",
            "66_layer2.3.BatchNorm2d_bn2                         [256]   [1, 256, 28, 28]   \n",
            "67_layer2.3.ReLU_relu                                   -   [1, 256, 28, 28]   \n",
            "68_layer2.3.Conv2d_conv3                 [256, 512, 1, 1]   [1, 512, 28, 28]   \n",
            "69_layer2.3.BatchNorm2d_bn3                         [512]   [1, 512, 28, 28]   \n",
            "70_layer2.3.ReLU_relu                                   -   [1, 512, 28, 28]   \n",
            "71_layer3.0.Conv2d_conv1                 [512, 512, 1, 1]   [1, 512, 28, 28]   \n",
            "72_layer3.0.BatchNorm2d_bn1                         [512]   [1, 512, 28, 28]   \n",
            "73_layer3.0.ReLU_relu                                   -   [1, 512, 28, 28]   \n",
            "74_layer3.0.Conv2d_conv2                  [16, 512, 3, 3]   [1, 512, 14, 14]   \n",
            "75_layer3.0.BatchNorm2d_bn2                         [512]   [1, 512, 14, 14]   \n",
            "76_layer3.0.ReLU_relu                                   -   [1, 512, 14, 14]   \n",
            "77_layer3.0.Conv2d_conv3                [512, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "78_layer3.0.BatchNorm2d_bn3                        [1024]  [1, 1024, 14, 14]   \n",
            "79_layer3.0.downsample.Conv2d_0         [512, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "80_layer3.0.downsample.BatchNorm2d_1               [1024]  [1, 1024, 14, 14]   \n",
            "81_layer3.0.ReLU_relu                                   -  [1, 1024, 14, 14]   \n",
            "82_layer3.1.Conv2d_conv1                [1024, 512, 1, 1]   [1, 512, 14, 14]   \n",
            "83_layer3.1.BatchNorm2d_bn1                         [512]   [1, 512, 14, 14]   \n",
            "84_layer3.1.ReLU_relu                                   -   [1, 512, 14, 14]   \n",
            "85_layer3.1.Conv2d_conv2                  [16, 512, 3, 3]   [1, 512, 14, 14]   \n",
            "86_layer3.1.BatchNorm2d_bn2                         [512]   [1, 512, 14, 14]   \n",
            "87_layer3.1.ReLU_relu                                   -   [1, 512, 14, 14]   \n",
            "88_layer3.1.Conv2d_conv3                [512, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "89_layer3.1.BatchNorm2d_bn3                        [1024]  [1, 1024, 14, 14]   \n",
            "90_layer3.1.ReLU_relu                                   -  [1, 1024, 14, 14]   \n",
            "91_layer3.2.Conv2d_conv1                [1024, 512, 1, 1]   [1, 512, 14, 14]   \n",
            "92_layer3.2.BatchNorm2d_bn1                         [512]   [1, 512, 14, 14]   \n",
            "93_layer3.2.ReLU_relu                                   -   [1, 512, 14, 14]   \n",
            "94_layer3.2.Conv2d_conv2                  [16, 512, 3, 3]   [1, 512, 14, 14]   \n",
            "95_layer3.2.BatchNorm2d_bn2                         [512]   [1, 512, 14, 14]   \n",
            "96_layer3.2.ReLU_relu                                   -   [1, 512, 14, 14]   \n",
            "97_layer3.2.Conv2d_conv3                [512, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "98_layer3.2.BatchNorm2d_bn3                        [1024]  [1, 1024, 14, 14]   \n",
            "99_layer3.2.ReLU_relu                                   -  [1, 1024, 14, 14]   \n",
            "100_layer3.3.Conv2d_conv1               [1024, 512, 1, 1]   [1, 512, 14, 14]   \n",
            "101_layer3.3.BatchNorm2d_bn1                        [512]   [1, 512, 14, 14]   \n",
            "102_layer3.3.ReLU_relu                                  -   [1, 512, 14, 14]   \n",
            "103_layer3.3.Conv2d_conv2                 [16, 512, 3, 3]   [1, 512, 14, 14]   \n",
            "104_layer3.3.BatchNorm2d_bn2                        [512]   [1, 512, 14, 14]   \n",
            "105_layer3.3.ReLU_relu                                  -   [1, 512, 14, 14]   \n",
            "106_layer3.3.Conv2d_conv3               [512, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "107_layer3.3.BatchNorm2d_bn3                       [1024]  [1, 1024, 14, 14]   \n",
            "108_layer3.3.ReLU_relu                                  -  [1, 1024, 14, 14]   \n",
            "109_layer3.4.Conv2d_conv1               [1024, 512, 1, 1]   [1, 512, 14, 14]   \n",
            "110_layer3.4.BatchNorm2d_bn1                        [512]   [1, 512, 14, 14]   \n",
            "111_layer3.4.ReLU_relu                                  -   [1, 512, 14, 14]   \n",
            "112_layer3.4.Conv2d_conv2                 [16, 512, 3, 3]   [1, 512, 14, 14]   \n",
            "113_layer3.4.BatchNorm2d_bn2                        [512]   [1, 512, 14, 14]   \n",
            "114_layer3.4.ReLU_relu                                  -   [1, 512, 14, 14]   \n",
            "115_layer3.4.Conv2d_conv3               [512, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "116_layer3.4.BatchNorm2d_bn3                       [1024]  [1, 1024, 14, 14]   \n",
            "117_layer3.4.ReLU_relu                                  -  [1, 1024, 14, 14]   \n",
            "118_layer3.5.Conv2d_conv1               [1024, 512, 1, 1]   [1, 512, 14, 14]   \n",
            "119_layer3.5.BatchNorm2d_bn1                        [512]   [1, 512, 14, 14]   \n",
            "120_layer3.5.ReLU_relu                                  -   [1, 512, 14, 14]   \n",
            "121_layer3.5.Conv2d_conv2                 [16, 512, 3, 3]   [1, 512, 14, 14]   \n",
            "122_layer3.5.BatchNorm2d_bn2                        [512]   [1, 512, 14, 14]   \n",
            "123_layer3.5.ReLU_relu                                  -   [1, 512, 14, 14]   \n",
            "124_layer3.5.Conv2d_conv3               [512, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "125_layer3.5.BatchNorm2d_bn3                       [1024]  [1, 1024, 14, 14]   \n",
            "126_layer3.5.ReLU_relu                                  -  [1, 1024, 14, 14]   \n",
            "127_layer4.0.Conv2d_conv1              [1024, 1024, 1, 1]  [1, 1024, 14, 14]   \n",
            "128_layer4.0.BatchNorm2d_bn1                       [1024]  [1, 1024, 14, 14]   \n",
            "129_layer4.0.ReLU_relu                                  -  [1, 1024, 14, 14]   \n",
            "130_layer4.0.Conv2d_conv2                [32, 1024, 3, 3]    [1, 1024, 7, 7]   \n",
            "131_layer4.0.BatchNorm2d_bn2                       [1024]    [1, 1024, 7, 7]   \n",
            "132_layer4.0.ReLU_relu                                  -    [1, 1024, 7, 7]   \n",
            "133_layer4.0.Conv2d_conv3              [1024, 2048, 1, 1]    [1, 2048, 7, 7]   \n",
            "134_layer4.0.BatchNorm2d_bn3                       [2048]    [1, 2048, 7, 7]   \n",
            "135_layer4.0.downsample.Conv2d_0       [1024, 2048, 1, 1]    [1, 2048, 7, 7]   \n",
            "136_layer4.0.downsample.BatchNorm2d_1              [2048]    [1, 2048, 7, 7]   \n",
            "137_layer4.0.ReLU_relu                                  -    [1, 2048, 7, 7]   \n",
            "138_layer4.1.Conv2d_conv1              [2048, 1024, 1, 1]    [1, 1024, 7, 7]   \n",
            "139_layer4.1.BatchNorm2d_bn1                       [1024]    [1, 1024, 7, 7]   \n",
            "140_layer4.1.ReLU_relu                                  -    [1, 1024, 7, 7]   \n",
            "141_layer4.1.Conv2d_conv2                [32, 1024, 3, 3]    [1, 1024, 7, 7]   \n",
            "142_layer4.1.BatchNorm2d_bn2                       [1024]    [1, 1024, 7, 7]   \n",
            "143_layer4.1.ReLU_relu                                  -    [1, 1024, 7, 7]   \n",
            "144_layer4.1.Conv2d_conv3              [1024, 2048, 1, 1]    [1, 2048, 7, 7]   \n",
            "145_layer4.1.BatchNorm2d_bn3                       [2048]    [1, 2048, 7, 7]   \n",
            "146_layer4.1.ReLU_relu                                  -    [1, 2048, 7, 7]   \n",
            "147_layer4.2.Conv2d_conv1              [2048, 1024, 1, 1]    [1, 1024, 7, 7]   \n",
            "148_layer4.2.BatchNorm2d_bn1                       [1024]    [1, 1024, 7, 7]   \n",
            "149_layer4.2.ReLU_relu                                  -    [1, 1024, 7, 7]   \n",
            "150_layer4.2.Conv2d_conv2                [32, 1024, 3, 3]    [1, 1024, 7, 7]   \n",
            "151_layer4.2.BatchNorm2d_bn2                       [1024]    [1, 1024, 7, 7]   \n",
            "152_layer4.2.ReLU_relu                                  -    [1, 1024, 7, 7]   \n",
            "153_layer4.2.Conv2d_conv3              [1024, 2048, 1, 1]    [1, 2048, 7, 7]   \n",
            "154_layer4.2.BatchNorm2d_bn3                       [2048]    [1, 2048, 7, 7]   \n",
            "155_layer4.2.ReLU_relu                                  -    [1, 2048, 7, 7]   \n",
            "156_avgpool                                             -    [1, 2048, 1, 1]   \n",
            "157_fc                                       [2048, 1000]          [1, 1000]   \n",
            "\n",
            "                                          Params    Mult-Adds  \n",
            "Layer                                                          \n",
            "0_conv1                                   9.408k  118.013952M  \n",
            "1_bn1                                      128.0         64.0  \n",
            "2_relu                                         -            -  \n",
            "3_maxpool                                      -            -  \n",
            "4_layer1.0.Conv2d_conv1                   8.192k   25.690112M  \n",
            "5_layer1.0.BatchNorm2d_bn1                 256.0        128.0  \n",
            "6_layer1.0.ReLU_relu                           -            -  \n",
            "7_layer1.0.Conv2d_conv2                   4.608k   14.450688M  \n",
            "8_layer1.0.BatchNorm2d_bn2                 256.0        128.0  \n",
            "9_layer1.0.ReLU_relu                           -            -  \n",
            "10_layer1.0.Conv2d_conv3                 32.768k  102.760448M  \n",
            "11_layer1.0.BatchNorm2d_bn3                512.0        256.0  \n",
            "12_layer1.0.downsample.Conv2d_0          16.384k   51.380224M  \n",
            "13_layer1.0.downsample.BatchNorm2d_1       512.0        256.0  \n",
            "14_layer1.0.ReLU_relu                          -            -  \n",
            "15_layer1.1.Conv2d_conv1                 32.768k  102.760448M  \n",
            "16_layer1.1.BatchNorm2d_bn1                256.0        128.0  \n",
            "17_layer1.1.ReLU_relu                          -            -  \n",
            "18_layer1.1.Conv2d_conv2                  4.608k   14.450688M  \n",
            "19_layer1.1.BatchNorm2d_bn2                256.0        128.0  \n",
            "20_layer1.1.ReLU_relu                          -            -  \n",
            "21_layer1.1.Conv2d_conv3                 32.768k  102.760448M  \n",
            "22_layer1.1.BatchNorm2d_bn3                512.0        256.0  \n",
            "23_layer1.1.ReLU_relu                          -            -  \n",
            "24_layer1.2.Conv2d_conv1                 32.768k  102.760448M  \n",
            "25_layer1.2.BatchNorm2d_bn1                256.0        128.0  \n",
            "26_layer1.2.ReLU_relu                          -            -  \n",
            "27_layer1.2.Conv2d_conv2                  4.608k   14.450688M  \n",
            "28_layer1.2.BatchNorm2d_bn2                256.0        128.0  \n",
            "29_layer1.2.ReLU_relu                          -            -  \n",
            "30_layer1.2.Conv2d_conv3                 32.768k  102.760448M  \n",
            "31_layer1.2.BatchNorm2d_bn3                512.0        256.0  \n",
            "32_layer1.2.ReLU_relu                          -            -  \n",
            "33_layer2.0.Conv2d_conv1                 65.536k  205.520896M  \n",
            "34_layer2.0.BatchNorm2d_bn1                512.0        256.0  \n",
            "35_layer2.0.ReLU_relu                          -            -  \n",
            "36_layer2.0.Conv2d_conv2                 18.432k   14.450688M  \n",
            "37_layer2.0.BatchNorm2d_bn2                512.0        256.0  \n",
            "38_layer2.0.ReLU_relu                          -            -  \n",
            "39_layer2.0.Conv2d_conv3                131.072k  102.760448M  \n",
            "40_layer2.0.BatchNorm2d_bn3               1.024k        512.0  \n",
            "41_layer2.0.downsample.Conv2d_0         131.072k  102.760448M  \n",
            "42_layer2.0.downsample.BatchNorm2d_1      1.024k        512.0  \n",
            "43_layer2.0.ReLU_relu                          -            -  \n",
            "44_layer2.1.Conv2d_conv1                131.072k  102.760448M  \n",
            "45_layer2.1.BatchNorm2d_bn1                512.0        256.0  \n",
            "46_layer2.1.ReLU_relu                          -            -  \n",
            "47_layer2.1.Conv2d_conv2                 18.432k   14.450688M  \n",
            "48_layer2.1.BatchNorm2d_bn2                512.0        256.0  \n",
            "49_layer2.1.ReLU_relu                          -            -  \n",
            "50_layer2.1.Conv2d_conv3                131.072k  102.760448M  \n",
            "51_layer2.1.BatchNorm2d_bn3               1.024k        512.0  \n",
            "52_layer2.1.ReLU_relu                          -            -  \n",
            "53_layer2.2.Conv2d_conv1                131.072k  102.760448M  \n",
            "54_layer2.2.BatchNorm2d_bn1                512.0        256.0  \n",
            "55_layer2.2.ReLU_relu                          -            -  \n",
            "56_layer2.2.Conv2d_conv2                 18.432k   14.450688M  \n",
            "57_layer2.2.BatchNorm2d_bn2                512.0        256.0  \n",
            "58_layer2.2.ReLU_relu                          -            -  \n",
            "59_layer2.2.Conv2d_conv3                131.072k  102.760448M  \n",
            "60_layer2.2.BatchNorm2d_bn3               1.024k        512.0  \n",
            "61_layer2.2.ReLU_relu                          -            -  \n",
            "62_layer2.3.Conv2d_conv1                131.072k  102.760448M  \n",
            "63_layer2.3.BatchNorm2d_bn1                512.0        256.0  \n",
            "64_layer2.3.ReLU_relu                          -            -  \n",
            "65_layer2.3.Conv2d_conv2                 18.432k   14.450688M  \n",
            "66_layer2.3.BatchNorm2d_bn2                512.0        256.0  \n",
            "67_layer2.3.ReLU_relu                          -            -  \n",
            "68_layer2.3.Conv2d_conv3                131.072k  102.760448M  \n",
            "69_layer2.3.BatchNorm2d_bn3               1.024k        512.0  \n",
            "70_layer2.3.ReLU_relu                          -            -  \n",
            "71_layer3.0.Conv2d_conv1                262.144k  205.520896M  \n",
            "72_layer3.0.BatchNorm2d_bn1               1.024k        512.0  \n",
            "73_layer3.0.ReLU_relu                          -            -  \n",
            "74_layer3.0.Conv2d_conv2                 73.728k   14.450688M  \n",
            "75_layer3.0.BatchNorm2d_bn2               1.024k        512.0  \n",
            "76_layer3.0.ReLU_relu                          -            -  \n",
            "77_layer3.0.Conv2d_conv3                524.288k  102.760448M  \n",
            "78_layer3.0.BatchNorm2d_bn3               2.048k       1.024k  \n",
            "79_layer3.0.downsample.Conv2d_0         524.288k  102.760448M  \n",
            "80_layer3.0.downsample.BatchNorm2d_1      2.048k       1.024k  \n",
            "81_layer3.0.ReLU_relu                          -            -  \n",
            "82_layer3.1.Conv2d_conv1                524.288k  102.760448M  \n",
            "83_layer3.1.BatchNorm2d_bn1               1.024k        512.0  \n",
            "84_layer3.1.ReLU_relu                          -            -  \n",
            "85_layer3.1.Conv2d_conv2                 73.728k   14.450688M  \n",
            "86_layer3.1.BatchNorm2d_bn2               1.024k        512.0  \n",
            "87_layer3.1.ReLU_relu                          -            -  \n",
            "88_layer3.1.Conv2d_conv3                524.288k  102.760448M  \n",
            "89_layer3.1.BatchNorm2d_bn3               2.048k       1.024k  \n",
            "90_layer3.1.ReLU_relu                          -            -  \n",
            "91_layer3.2.Conv2d_conv1                524.288k  102.760448M  \n",
            "92_layer3.2.BatchNorm2d_bn1               1.024k        512.0  \n",
            "93_layer3.2.ReLU_relu                          -            -  \n",
            "94_layer3.2.Conv2d_conv2                 73.728k   14.450688M  \n",
            "95_layer3.2.BatchNorm2d_bn2               1.024k        512.0  \n",
            "96_layer3.2.ReLU_relu                          -            -  \n",
            "97_layer3.2.Conv2d_conv3                524.288k  102.760448M  \n",
            "98_layer3.2.BatchNorm2d_bn3               2.048k       1.024k  \n",
            "99_layer3.2.ReLU_relu                          -            -  \n",
            "100_layer3.3.Conv2d_conv1               524.288k  102.760448M  \n",
            "101_layer3.3.BatchNorm2d_bn1              1.024k        512.0  \n",
            "102_layer3.3.ReLU_relu                         -            -  \n",
            "103_layer3.3.Conv2d_conv2                73.728k   14.450688M  \n",
            "104_layer3.3.BatchNorm2d_bn2              1.024k        512.0  \n",
            "105_layer3.3.ReLU_relu                         -            -  \n",
            "106_layer3.3.Conv2d_conv3               524.288k  102.760448M  \n",
            "107_layer3.3.BatchNorm2d_bn3              2.048k       1.024k  \n",
            "108_layer3.3.ReLU_relu                         -            -  \n",
            "109_layer3.4.Conv2d_conv1               524.288k  102.760448M  \n",
            "110_layer3.4.BatchNorm2d_bn1              1.024k        512.0  \n",
            "111_layer3.4.ReLU_relu                         -            -  \n",
            "112_layer3.4.Conv2d_conv2                73.728k   14.450688M  \n",
            "113_layer3.4.BatchNorm2d_bn2              1.024k        512.0  \n",
            "114_layer3.4.ReLU_relu                         -            -  \n",
            "115_layer3.4.Conv2d_conv3               524.288k  102.760448M  \n",
            "116_layer3.4.BatchNorm2d_bn3              2.048k       1.024k  \n",
            "117_layer3.4.ReLU_relu                         -            -  \n",
            "118_layer3.5.Conv2d_conv1               524.288k  102.760448M  \n",
            "119_layer3.5.BatchNorm2d_bn1              1.024k        512.0  \n",
            "120_layer3.5.ReLU_relu                         -            -  \n",
            "121_layer3.5.Conv2d_conv2                73.728k   14.450688M  \n",
            "122_layer3.5.BatchNorm2d_bn2              1.024k        512.0  \n",
            "123_layer3.5.ReLU_relu                         -            -  \n",
            "124_layer3.5.Conv2d_conv3               524.288k  102.760448M  \n",
            "125_layer3.5.BatchNorm2d_bn3              2.048k       1.024k  \n",
            "126_layer3.5.ReLU_relu                         -            -  \n",
            "127_layer4.0.Conv2d_conv1              1.048576M  205.520896M  \n",
            "128_layer4.0.BatchNorm2d_bn1              2.048k       1.024k  \n",
            "129_layer4.0.ReLU_relu                         -            -  \n",
            "130_layer4.0.Conv2d_conv2               294.912k   14.450688M  \n",
            "131_layer4.0.BatchNorm2d_bn2              2.048k       1.024k  \n",
            "132_layer4.0.ReLU_relu                         -            -  \n",
            "133_layer4.0.Conv2d_conv3              2.097152M  102.760448M  \n",
            "134_layer4.0.BatchNorm2d_bn3              4.096k       2.048k  \n",
            "135_layer4.0.downsample.Conv2d_0       2.097152M  102.760448M  \n",
            "136_layer4.0.downsample.BatchNorm2d_1     4.096k       2.048k  \n",
            "137_layer4.0.ReLU_relu                         -            -  \n",
            "138_layer4.1.Conv2d_conv1              2.097152M  102.760448M  \n",
            "139_layer4.1.BatchNorm2d_bn1              2.048k       1.024k  \n",
            "140_layer4.1.ReLU_relu                         -            -  \n",
            "141_layer4.1.Conv2d_conv2               294.912k   14.450688M  \n",
            "142_layer4.1.BatchNorm2d_bn2              2.048k       1.024k  \n",
            "143_layer4.1.ReLU_relu                         -            -  \n",
            "144_layer4.1.Conv2d_conv3              2.097152M  102.760448M  \n",
            "145_layer4.1.BatchNorm2d_bn3              4.096k       2.048k  \n",
            "146_layer4.1.ReLU_relu                         -            -  \n",
            "147_layer4.2.Conv2d_conv1              2.097152M  102.760448M  \n",
            "148_layer4.2.BatchNorm2d_bn1              2.048k       1.024k  \n",
            "149_layer4.2.ReLU_relu                         -            -  \n",
            "150_layer4.2.Conv2d_conv2               294.912k   14.450688M  \n",
            "151_layer4.2.BatchNorm2d_bn2              2.048k       1.024k  \n",
            "152_layer4.2.ReLU_relu                         -            -  \n",
            "153_layer4.2.Conv2d_conv3              2.097152M  102.760448M  \n",
            "154_layer4.2.BatchNorm2d_bn3              4.096k       2.048k  \n",
            "155_layer4.2.ReLU_relu                         -            -  \n",
            "156_avgpool                                    -            -  \n",
            "157_fc                                    2.049M       2.048M  \n",
            "----------------------------------------------------------------------------------------------------\n",
            "                            Totals\n",
            "Total params            25.028904M\n",
            "Trainable params        25.028904M\n",
            "Non-trainable params           0.0\n",
            "Mult-Adds             4.230513984G\n",
            "====================================================================================================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Kernel Shape</th>\n",
              "      <th>Output Shape</th>\n",
              "      <th>Params</th>\n",
              "      <th>Mult-Adds</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Layer</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0_conv1</th>\n",
              "      <td>[3, 64, 7, 7]</td>\n",
              "      <td>[1, 64, 112, 112]</td>\n",
              "      <td>9408.0</td>\n",
              "      <td>118013952.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1_bn1</th>\n",
              "      <td>[64]</td>\n",
              "      <td>[1, 64, 112, 112]</td>\n",
              "      <td>128.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2_relu</th>\n",
              "      <td>-</td>\n",
              "      <td>[1, 64, 112, 112]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3_maxpool</th>\n",
              "      <td>-</td>\n",
              "      <td>[1, 64, 56, 56]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4_layer1.0.Conv2d_conv1</th>\n",
              "      <td>[64, 128, 1, 1]</td>\n",
              "      <td>[1, 128, 56, 56]</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>25690112.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153_layer4.2.Conv2d_conv3</th>\n",
              "      <td>[1024, 2048, 1, 1]</td>\n",
              "      <td>[1, 2048, 7, 7]</td>\n",
              "      <td>2097152.0</td>\n",
              "      <td>102760448.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154_layer4.2.BatchNorm2d_bn3</th>\n",
              "      <td>[2048]</td>\n",
              "      <td>[1, 2048, 7, 7]</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>2048.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155_layer4.2.ReLU_relu</th>\n",
              "      <td>-</td>\n",
              "      <td>[1, 2048, 7, 7]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156_avgpool</th>\n",
              "      <td>-</td>\n",
              "      <td>[1, 2048, 1, 1]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157_fc</th>\n",
              "      <td>[2048, 1000]</td>\n",
              "      <td>[1, 1000]</td>\n",
              "      <td>2049000.0</td>\n",
              "      <td>2048000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>158 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Kernel Shape  ...    Mult-Adds\n",
              "Layer                                             ...             \n",
              "0_conv1                            [3, 64, 7, 7]  ...  118013952.0\n",
              "1_bn1                                       [64]  ...         64.0\n",
              "2_relu                                         -  ...          NaN\n",
              "3_maxpool                                      -  ...          NaN\n",
              "4_layer1.0.Conv2d_conv1          [64, 128, 1, 1]  ...   25690112.0\n",
              "...                                          ...  ...          ...\n",
              "153_layer4.2.Conv2d_conv3     [1024, 2048, 1, 1]  ...  102760448.0\n",
              "154_layer4.2.BatchNorm2d_bn3              [2048]  ...       2048.0\n",
              "155_layer4.2.ReLU_relu                         -  ...          NaN\n",
              "156_avgpool                                    -  ...          NaN\n",
              "157_fc                              [2048, 1000]  ...    2048000.0\n",
              "\n",
              "[158 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lendqIHEEMvh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model.named_modules"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBcLbrveEMyg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6fe53cd7-53ea-414f-be10-d6f85d2083ba"
      },
      "source": [
        "new_model = model\n",
        "for name, module in new_model.named_modules():\n",
        "    # prune 20% of connections in all 2D-conv layers\n",
        "    if isinstance(module, torch.nn.Conv2d):\n",
        "        prune.l1_unstructured(module, name='weight', amount=0.2)\n",
        "    # prune 40% of connections in all linear layers\n",
        "    elif isinstance(module, torch.nn.Linear):\n",
        "        prune.l1_unstructured(module, name='weight', amount=0.4)\n",
        "\n",
        "print(dict(new_model.named_buffers()).keys())  # to verify that all masks exist"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['conv1.weight_mask', 'bn1.running_mean', 'bn1.running_var', 'bn1.num_batches_tracked', 'layer1.0.conv1.weight_mask', 'layer1.0.bn1.running_mean', 'layer1.0.bn1.running_var', 'layer1.0.bn1.num_batches_tracked', 'layer1.0.conv2.weight_mask', 'layer1.0.bn2.running_mean', 'layer1.0.bn2.running_var', 'layer1.0.bn2.num_batches_tracked', 'layer1.0.conv3.weight_mask', 'layer1.0.bn3.running_mean', 'layer1.0.bn3.running_var', 'layer1.0.bn3.num_batches_tracked', 'layer1.0.downsample.0.weight_mask', 'layer1.0.downsample.1.running_mean', 'layer1.0.downsample.1.running_var', 'layer1.0.downsample.1.num_batches_tracked', 'layer1.1.conv1.weight_mask', 'layer1.1.bn1.running_mean', 'layer1.1.bn1.running_var', 'layer1.1.bn1.num_batches_tracked', 'layer1.1.conv2.weight_mask', 'layer1.1.bn2.running_mean', 'layer1.1.bn2.running_var', 'layer1.1.bn2.num_batches_tracked', 'layer1.1.conv3.weight_mask', 'layer1.1.bn3.running_mean', 'layer1.1.bn3.running_var', 'layer1.1.bn3.num_batches_tracked', 'layer1.2.conv1.weight_mask', 'layer1.2.bn1.running_mean', 'layer1.2.bn1.running_var', 'layer1.2.bn1.num_batches_tracked', 'layer1.2.conv2.weight_mask', 'layer1.2.bn2.running_mean', 'layer1.2.bn2.running_var', 'layer1.2.bn2.num_batches_tracked', 'layer1.2.conv3.weight_mask', 'layer1.2.bn3.running_mean', 'layer1.2.bn3.running_var', 'layer1.2.bn3.num_batches_tracked', 'layer2.0.conv1.weight_mask', 'layer2.0.bn1.running_mean', 'layer2.0.bn1.running_var', 'layer2.0.bn1.num_batches_tracked', 'layer2.0.conv2.weight_mask', 'layer2.0.bn2.running_mean', 'layer2.0.bn2.running_var', 'layer2.0.bn2.num_batches_tracked', 'layer2.0.conv3.weight_mask', 'layer2.0.bn3.running_mean', 'layer2.0.bn3.running_var', 'layer2.0.bn3.num_batches_tracked', 'layer2.0.downsample.0.weight_mask', 'layer2.0.downsample.1.running_mean', 'layer2.0.downsample.1.running_var', 'layer2.0.downsample.1.num_batches_tracked', 'layer2.1.conv1.weight_mask', 'layer2.1.bn1.running_mean', 'layer2.1.bn1.running_var', 'layer2.1.bn1.num_batches_tracked', 'layer2.1.conv2.weight_mask', 'layer2.1.bn2.running_mean', 'layer2.1.bn2.running_var', 'layer2.1.bn2.num_batches_tracked', 'layer2.1.conv3.weight_mask', 'layer2.1.bn3.running_mean', 'layer2.1.bn3.running_var', 'layer2.1.bn3.num_batches_tracked', 'layer2.2.conv1.weight_mask', 'layer2.2.bn1.running_mean', 'layer2.2.bn1.running_var', 'layer2.2.bn1.num_batches_tracked', 'layer2.2.conv2.weight_mask', 'layer2.2.bn2.running_mean', 'layer2.2.bn2.running_var', 'layer2.2.bn2.num_batches_tracked', 'layer2.2.conv3.weight_mask', 'layer2.2.bn3.running_mean', 'layer2.2.bn3.running_var', 'layer2.2.bn3.num_batches_tracked', 'layer2.3.conv1.weight_mask', 'layer2.3.bn1.running_mean', 'layer2.3.bn1.running_var', 'layer2.3.bn1.num_batches_tracked', 'layer2.3.conv2.weight_mask', 'layer2.3.bn2.running_mean', 'layer2.3.bn2.running_var', 'layer2.3.bn2.num_batches_tracked', 'layer2.3.conv3.weight_mask', 'layer2.3.bn3.running_mean', 'layer2.3.bn3.running_var', 'layer2.3.bn3.num_batches_tracked', 'layer3.0.conv1.weight_mask', 'layer3.0.bn1.running_mean', 'layer3.0.bn1.running_var', 'layer3.0.bn1.num_batches_tracked', 'layer3.0.conv2.weight_mask', 'layer3.0.bn2.running_mean', 'layer3.0.bn2.running_var', 'layer3.0.bn2.num_batches_tracked', 'layer3.0.conv3.weight_mask', 'layer3.0.bn3.running_mean', 'layer3.0.bn3.running_var', 'layer3.0.bn3.num_batches_tracked', 'layer3.0.downsample.0.weight_mask', 'layer3.0.downsample.1.running_mean', 'layer3.0.downsample.1.running_var', 'layer3.0.downsample.1.num_batches_tracked', 'layer3.1.conv1.weight_mask', 'layer3.1.bn1.running_mean', 'layer3.1.bn1.running_var', 'layer3.1.bn1.num_batches_tracked', 'layer3.1.conv2.weight_mask', 'layer3.1.bn2.running_mean', 'layer3.1.bn2.running_var', 'layer3.1.bn2.num_batches_tracked', 'layer3.1.conv3.weight_mask', 'layer3.1.bn3.running_mean', 'layer3.1.bn3.running_var', 'layer3.1.bn3.num_batches_tracked', 'layer3.2.conv1.weight_mask', 'layer3.2.bn1.running_mean', 'layer3.2.bn1.running_var', 'layer3.2.bn1.num_batches_tracked', 'layer3.2.conv2.weight_mask', 'layer3.2.bn2.running_mean', 'layer3.2.bn2.running_var', 'layer3.2.bn2.num_batches_tracked', 'layer3.2.conv3.weight_mask', 'layer3.2.bn3.running_mean', 'layer3.2.bn3.running_var', 'layer3.2.bn3.num_batches_tracked', 'layer3.3.conv1.weight_mask', 'layer3.3.bn1.running_mean', 'layer3.3.bn1.running_var', 'layer3.3.bn1.num_batches_tracked', 'layer3.3.conv2.weight_mask', 'layer3.3.bn2.running_mean', 'layer3.3.bn2.running_var', 'layer3.3.bn2.num_batches_tracked', 'layer3.3.conv3.weight_mask', 'layer3.3.bn3.running_mean', 'layer3.3.bn3.running_var', 'layer3.3.bn3.num_batches_tracked', 'layer3.4.conv1.weight_mask', 'layer3.4.bn1.running_mean', 'layer3.4.bn1.running_var', 'layer3.4.bn1.num_batches_tracked', 'layer3.4.conv2.weight_mask', 'layer3.4.bn2.running_mean', 'layer3.4.bn2.running_var', 'layer3.4.bn2.num_batches_tracked', 'layer3.4.conv3.weight_mask', 'layer3.4.bn3.running_mean', 'layer3.4.bn3.running_var', 'layer3.4.bn3.num_batches_tracked', 'layer3.5.conv1.weight_mask', 'layer3.5.bn1.running_mean', 'layer3.5.bn1.running_var', 'layer3.5.bn1.num_batches_tracked', 'layer3.5.conv2.weight_mask', 'layer3.5.bn2.running_mean', 'layer3.5.bn2.running_var', 'layer3.5.bn2.num_batches_tracked', 'layer3.5.conv3.weight_mask', 'layer3.5.bn3.running_mean', 'layer3.5.bn3.running_var', 'layer3.5.bn3.num_batches_tracked', 'layer4.0.conv1.weight_mask', 'layer4.0.bn1.running_mean', 'layer4.0.bn1.running_var', 'layer4.0.bn1.num_batches_tracked', 'layer4.0.conv2.weight_mask', 'layer4.0.bn2.running_mean', 'layer4.0.bn2.running_var', 'layer4.0.bn2.num_batches_tracked', 'layer4.0.conv3.weight_mask', 'layer4.0.bn3.running_mean', 'layer4.0.bn3.running_var', 'layer4.0.bn3.num_batches_tracked', 'layer4.0.downsample.0.weight_mask', 'layer4.0.downsample.1.running_mean', 'layer4.0.downsample.1.running_var', 'layer4.0.downsample.1.num_batches_tracked', 'layer4.1.conv1.weight_mask', 'layer4.1.bn1.running_mean', 'layer4.1.bn1.running_var', 'layer4.1.bn1.num_batches_tracked', 'layer4.1.conv2.weight_mask', 'layer4.1.bn2.running_mean', 'layer4.1.bn2.running_var', 'layer4.1.bn2.num_batches_tracked', 'layer4.1.conv3.weight_mask', 'layer4.1.bn3.running_mean', 'layer4.1.bn3.running_var', 'layer4.1.bn3.num_batches_tracked', 'layer4.2.conv1.weight_mask', 'layer4.2.bn1.running_mean', 'layer4.2.bn1.running_var', 'layer4.2.bn1.num_batches_tracked', 'layer4.2.conv2.weight_mask', 'layer4.2.bn2.running_mean', 'layer4.2.bn2.running_var', 'layer4.2.bn2.num_batches_tracked', 'layer4.2.conv3.weight_mask', 'layer4.2.bn3.running_mean', 'layer4.2.bn3.running_var', 'layer4.2.bn3.num_batches_tracked', 'fc.weight_mask'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kkur6SnMEM6C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model.state_dict();\n",
        "PATH = save_dir + '/Resnet50_pruned.pth'\n",
        "torch.save(new_model.state_dict(), PATH)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83gb6LYOEM86",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e34dfd8b-b2a1-4e92-fc70-86fe47a6f5fa"
      },
      "source": [
        "new_model#.layer1.state_dict().keys()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (4): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (5): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlLffLjsEIXE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}