model info of 3D CNNs.ipynb

model-info-of-3d-cnns.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/gist/tttamaki/6c09a6c9b34c23fe897373f4e45797c2/model-info-of-3d-cnns.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": "DGVb1q_dDdtZ",
        "outputId": "ae59a169-6b76-4181-8ab8-2cd2bcda0cdc"
      },
      "source": [
        "!pip install torchinfo\n",
        "!pip install git+https://github.com/facebookresearch/fvcore.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torchinfo\n",
            "  Downloading torchinfo-1.5.3-py3-none-any.whl (19 kB)\n",
            "Installing collected packages: torchinfo\n",
            "Successfully installed torchinfo-1.5.3\n",
            "Collecting git+https://github.com/facebookresearch/fvcore.git\n",
            "  Cloning https://github.com/facebookresearch/fvcore.git to /tmp/pip-req-build-9fnajitk\n",
            "  Running command git clone -q https://github.com/facebookresearch/fvcore.git /tmp/pip-req-build-9fnajitk\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from fvcore==0.1.5) (1.19.5)\n",
            "Collecting yacs>=0.1.6\n",
            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 12.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from fvcore==0.1.5) (4.62.0)\n",
            "Requirement already satisfied: termcolor>=1.1 in /usr/local/lib/python3.7/dist-packages (from fvcore==0.1.5) (1.1.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from fvcore==0.1.5) (7.1.2)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from fvcore==0.1.5) (0.8.9)\n",
            "Collecting iopath>=0.1.7\n",
            "  Downloading iopath-0.1.9-py3-none-any.whl (27 kB)\n",
            "Collecting portalocker\n",
            "  Downloading portalocker-2.3.2-py2.py3-none-any.whl (15 kB)\n",
            "Building wheels for collected packages: fvcore\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fvcore: filename=fvcore-0.1.5-py3-none-any.whl size=64542 sha256=527de52a215ce938ed10d9683b8e94365bf25e8aaad1a5d9a4fa061a2a614977\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-qmgvfcyv/wheels/24/1d/09/8167de727fe5b74f832b6fcb5d9069d8f03ca29f337bfe484d\n",
            "Successfully built fvcore\n",
            "Installing collected packages: pyyaml, portalocker, yacs, iopath, fvcore\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed fvcore-0.1.5 iopath-0.1.9 portalocker-2.3.2 pyyaml-5.4.1 yacs-0.1.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LI-SbyseDoNB"
      },
      "source": [
        "import torch\n",
        "import torchinfo"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yiiigMaJKzkD"
      },
      "source": [
        "# X3D\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "dc29b9fe5b904a92bda232ab6431a1dc",
            "a393ba13b45e4265bf505ae688b060c9",
            "6284203dd9fd4fe7b07b1c5dc224e210",
            "af5aec4c7f074715ac463f6b3f73ff70",
            "57729c7574a84aba9804b1ee4ea42a2f",
            "146d8f7bfb454b0eb43ac176265e6619",
            "d59849bef9c748eb9d450a3b2df51052",
            "8e886b0437c24a988848fb894f868998",
            "b59fc614126241379d4f65f4139c2d5a",
            "efb061a2f00b417a8e9e7c4c9b60de9c",
            "e49f63992625494a8a32de17f54e56d9"
          ]
        },
        "id": "ERkfp4TfDtv5",
        "outputId": "5bb3bb33-4867-4364-9f29-71447037f9b1"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/X3D_L.yaml/\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'x3d_l', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 16\n",
        "size = 312\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, size, size),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/facebookresearch/pytorchvideo/archive/master.zip\" to /root/.cache/torch/hub/master.zip\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/X3D_L.pyth\" to /root/.cache/torch/hub/checkpoints/X3D_L.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc29b9fe5b904a92bda232ab6431a1dc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/47.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==============================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape               Output Shape\n",
              "==============================================================================================================\n",
              "Net                                                          --                        --\n",
              "├─ModuleList (blocks)                                        --                        --\n",
              "│    └─ResStage (1)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (2)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (3)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (4)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResNetBasicStem (0)                                   [1, 3, 16, 312, 312]      [1, 24, 16, 156, 156]\n",
              "│    │    └─Conv2plus1d (conv)                               [1, 3, 16, 312, 312]      [1, 24, 16, 156, 156]\n",
              "│    │    │    └─Conv3d (conv_t)                             [1, 3, 16, 312, 312]      [1, 24, 16, 156, 156]\n",
              "│    │    │    └─Conv3d (conv_xy)                            [1, 24, 16, 156, 156]     [1, 24, 16, 156, 156]\n",
              "│    │    └─BatchNorm3d (norm)                               [1, 24, 16, 156, 156]     [1, 24, 16, 156, 156]\n",
              "│    │    └─ReLU (activation)                                [1, 24, 16, 156, 156]     [1, 24, 16, 156, 156]\n",
              "│    └─ResStage (1)                                          [1, 24, 16, 156, 156]     [1, 24, 16, 78, 78]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 24, 16, 156, 156]     [1, 24, 16, 78, 78]\n",
              "│    │    │    └─ResBlock (1)                                [1, 24, 16, 78, 78]       [1, 24, 16, 78, 78]\n",
              "│    │    │    └─ResBlock (2)                                [1, 24, 16, 78, 78]       [1, 24, 16, 78, 78]\n",
              "│    │    │    └─ResBlock (3)                                [1, 24, 16, 78, 78]       [1, 24, 16, 78, 78]\n",
              "│    │    │    └─ResBlock (4)                                [1, 24, 16, 78, 78]       [1, 24, 16, 78, 78]\n",
              "│    └─ResStage (2)                                          [1, 24, 16, 78, 78]       [1, 48, 16, 39, 39]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 24, 16, 78, 78]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (1)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (2)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (3)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (4)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (5)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (6)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (7)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (8)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    │    │    └─ResBlock (9)                                [1, 48, 16, 39, 39]       [1, 48, 16, 39, 39]\n",
              "│    └─ResStage (3)                                          [1, 48, 16, 39, 39]       [1, 96, 16, 20, 20]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 48, 16, 39, 39]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (1)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (2)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (3)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (4)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (5)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (6)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (7)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (8)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (9)                                [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (10)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (11)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (12)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (13)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (14)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (15)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (16)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (17)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (18)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (19)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (20)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (21)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (22)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (23)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    │    │    └─ResBlock (24)                               [1, 96, 16, 20, 20]       [1, 96, 16, 20, 20]\n",
              "│    └─ResStage (4)                                          [1, 96, 16, 20, 20]       [1, 192, 16, 10, 10]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 96, 16, 20, 20]       [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (1)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (2)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (3)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (4)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (5)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (6)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (7)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (8)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (9)                                [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (10)                               [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (11)                               [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (12)                               [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (13)                               [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    │    │    └─ResBlock (14)                               [1, 192, 16, 10, 10]      [1, 192, 16, 10, 10]\n",
              "│    └─ResNetBasicHead (5)                                   [1, 192, 16, 10, 10]      [1, 400]\n",
              "│    │    └─ProjectedPool (pool)                             [1, 192, 16, 10, 10]      [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─Conv3d (pre_conv)                           [1, 192, 16, 10, 10]      [1, 432, 16, 10, 10]\n",
              "│    │    │    └─BatchNorm3d (pre_norm)                      [1, 432, 16, 10, 10]      [1, 432, 16, 10, 10]\n",
              "│    │    │    └─ReLU (pre_act)                              [1, 432, 16, 10, 10]      [1, 432, 16, 10, 10]\n",
              "│    │    │    └─AvgPool3d (pool)                            [1, 432, 16, 10, 10]      [1, 432, 1, 1, 1]\n",
              "│    │    │    └─Conv3d (post_conv)                          [1, 432, 1, 1, 1]         [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─ReLU (post_act)                             [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Dropout (dropout)                                [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                                    [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─Softmax (activation)                             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)                  [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "==============================================================================================================\n",
              "Total params: 6,153,384\n",
              "Trainable params: 6,153,384\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 18.37\n",
              "==============================================================================================================\n",
              "Input size (MB): 18.69\n",
              "Forward/backward pass size (MB): 4574.27\n",
              "Params size (MB): 24.61\n",
              "Estimated Total Size (MB): 4617.58\n",
              "=============================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 1000,
          "referenced_widgets": [
            "3040895f4a884e8a9bce6be6a5338d97",
            "58ee6ba46da14fffbd16c5aafc14b2c8",
            "490f6f5dcae74292905074d86f6958d7",
            "ae7e87b0187043c189ed105c4bcceb6a",
            "c5a06f1cda4248c8aaa2305a979f2873",
            "069fe2bf20bc4a0a937e83dbbb70eed2",
            "d8840339d41b49b0812e49d3a11a5fbf",
            "3928ddfc57ed48dfaa5156b6f3d32078",
            "ef765e35b0784445a2205635656f9421",
            "750ed6d2d3e14d7c912020cf973a2dae",
            "364d5f3106d64319afc9e0e3ee9c52cc"
          ]
        },
        "id": "tfeeOF-pEBc7",
        "outputId": "b78b07c5-76ce-4106-8762-c0a73b5215ec"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/X3D_M.yaml/\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'x3d_m', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 16\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, 224, 224),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/X3D_M.pyth\" to /root/.cache/torch/hub/checkpoints/X3D_M.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3040895f4a884e8a9bce6be6a5338d97",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/29.4M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==============================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape               Output Shape\n",
              "==============================================================================================================\n",
              "Net                                                          --                        --\n",
              "├─ModuleList (blocks)                                        --                        --\n",
              "│    └─ResStage (1)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (2)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (3)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (4)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResNetBasicStem (0)                                   [1, 3, 16, 224, 224]      [1, 24, 16, 112, 112]\n",
              "│    │    └─Conv2plus1d (conv)                               [1, 3, 16, 224, 224]      [1, 24, 16, 112, 112]\n",
              "│    │    │    └─Conv3d (conv_t)                             [1, 3, 16, 224, 224]      [1, 24, 16, 112, 112]\n",
              "│    │    │    └─Conv3d (conv_xy)                            [1, 24, 16, 112, 112]     [1, 24, 16, 112, 112]\n",
              "│    │    └─BatchNorm3d (norm)                               [1, 24, 16, 112, 112]     [1, 24, 16, 112, 112]\n",
              "│    │    └─ReLU (activation)                                [1, 24, 16, 112, 112]     [1, 24, 16, 112, 112]\n",
              "│    └─ResStage (1)                                          [1, 24, 16, 112, 112]     [1, 24, 16, 56, 56]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 24, 16, 112, 112]     [1, 24, 16, 56, 56]\n",
              "│    │    │    └─ResBlock (1)                                [1, 24, 16, 56, 56]       [1, 24, 16, 56, 56]\n",
              "│    │    │    └─ResBlock (2)                                [1, 24, 16, 56, 56]       [1, 24, 16, 56, 56]\n",
              "│    └─ResStage (2)                                          [1, 24, 16, 56, 56]       [1, 48, 16, 28, 28]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 24, 16, 56, 56]       [1, 48, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (1)                                [1, 48, 16, 28, 28]       [1, 48, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (2)                                [1, 48, 16, 28, 28]       [1, 48, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (3)                                [1, 48, 16, 28, 28]       [1, 48, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (4)                                [1, 48, 16, 28, 28]       [1, 48, 16, 28, 28]\n",
              "│    └─ResStage (3)                                          [1, 48, 16, 28, 28]       [1, 96, 16, 14, 14]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 48, 16, 28, 28]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (1)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (2)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (3)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (4)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (5)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (6)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (7)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (8)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (9)                                [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    │    │    └─ResBlock (10)                               [1, 96, 16, 14, 14]       [1, 96, 16, 14, 14]\n",
              "│    └─ResStage (4)                                          [1, 96, 16, 14, 14]       [1, 192, 16, 7, 7]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 96, 16, 14, 14]       [1, 192, 16, 7, 7]\n",
              "│    │    │    └─ResBlock (1)                                [1, 192, 16, 7, 7]        [1, 192, 16, 7, 7]\n",
              "│    │    │    └─ResBlock (2)                                [1, 192, 16, 7, 7]        [1, 192, 16, 7, 7]\n",
              "│    │    │    └─ResBlock (3)                                [1, 192, 16, 7, 7]        [1, 192, 16, 7, 7]\n",
              "│    │    │    └─ResBlock (4)                                [1, 192, 16, 7, 7]        [1, 192, 16, 7, 7]\n",
              "│    │    │    └─ResBlock (5)                                [1, 192, 16, 7, 7]        [1, 192, 16, 7, 7]\n",
              "│    │    │    └─ResBlock (6)                                [1, 192, 16, 7, 7]        [1, 192, 16, 7, 7]\n",
              "│    └─ResNetBasicHead (5)                                   [1, 192, 16, 7, 7]        [1, 400]\n",
              "│    │    └─ProjectedPool (pool)                             [1, 192, 16, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─Conv3d (pre_conv)                           [1, 192, 16, 7, 7]        [1, 432, 16, 7, 7]\n",
              "│    │    │    └─BatchNorm3d (pre_norm)                      [1, 432, 16, 7, 7]        [1, 432, 16, 7, 7]\n",
              "│    │    │    └─ReLU (pre_act)                              [1, 432, 16, 7, 7]        [1, 432, 16, 7, 7]\n",
              "│    │    │    └─AvgPool3d (pool)                            [1, 432, 16, 7, 7]        [1, 432, 1, 1, 1]\n",
              "│    │    │    └─Conv3d (post_conv)                          [1, 432, 1, 1, 1]         [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─ReLU (post_act)                             [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Dropout (dropout)                                [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                                    [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─Softmax (activation)                             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)                  [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "==============================================================================================================\n",
              "Total params: 3,794,274\n",
              "Trainable params: 3,794,274\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 4.73\n",
              "==============================================================================================================\n",
              "Input size (MB): 9.63\n",
              "Forward/backward pass size (MB): 1358.41\n",
              "Params size (MB): 15.18\n",
              "Estimated Total Size (MB): 1383.22\n",
              "=============================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "9976df456b04424a840823088d517bfe",
            "cf0d9d62b4434eb7bd1838b402e24e98",
            "5d3396ea651b46ada5313418f50da63e",
            "f6f5d819c67048668becf14bd6ccc721",
            "1d1e15aa19874f59a012247ba0b8e495",
            "36e8162b191140308ed37096046437ff",
            "9385c6a6915544aeb9f0a1388a6af8f0",
            "a0187193d9c743d5af9119838b59dc88",
            "db0db06e39624f1fa91099678f66317a",
            "47cf04bec775462bac569839e2442bec",
            "0e98f9fee1ec40c6b822ec94f4ff5d2a"
          ]
        },
        "id": "NJtryREjOrVG",
        "outputId": "988fb3e7-6eb2-405f-ea3f-0d244db96d9d"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/X3D_S.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'x3d_s', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 13\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, 160, 160),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/X3D_S.pyth\" to /root/.cache/torch/hub/checkpoints/X3D_S.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9976df456b04424a840823088d517bfe",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/29.4M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==============================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape               Output Shape\n",
              "==============================================================================================================\n",
              "Net                                                          --                        --\n",
              "├─ModuleList (blocks)                                        --                        --\n",
              "│    └─ResStage (1)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (2)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (3)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResStage (4)                                          --                        --\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    └─ResNetBasicStem (0)                                   [1, 3, 13, 160, 160]      [1, 24, 13, 80, 80]\n",
              "│    │    └─Conv2plus1d (conv)                               [1, 3, 13, 160, 160]      [1, 24, 13, 80, 80]\n",
              "│    │    │    └─Conv3d (conv_t)                             [1, 3, 13, 160, 160]      [1, 24, 13, 80, 80]\n",
              "│    │    │    └─Conv3d (conv_xy)                            [1, 24, 13, 80, 80]       [1, 24, 13, 80, 80]\n",
              "│    │    └─BatchNorm3d (norm)                               [1, 24, 13, 80, 80]       [1, 24, 13, 80, 80]\n",
              "│    │    └─ReLU (activation)                                [1, 24, 13, 80, 80]       [1, 24, 13, 80, 80]\n",
              "│    └─ResStage (1)                                          [1, 24, 13, 80, 80]       [1, 24, 13, 40, 40]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 24, 13, 80, 80]       [1, 24, 13, 40, 40]\n",
              "│    │    │    └─ResBlock (1)                                [1, 24, 13, 40, 40]       [1, 24, 13, 40, 40]\n",
              "│    │    │    └─ResBlock (2)                                [1, 24, 13, 40, 40]       [1, 24, 13, 40, 40]\n",
              "│    └─ResStage (2)                                          [1, 24, 13, 40, 40]       [1, 48, 13, 20, 20]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 24, 13, 40, 40]       [1, 48, 13, 20, 20]\n",
              "│    │    │    └─ResBlock (1)                                [1, 48, 13, 20, 20]       [1, 48, 13, 20, 20]\n",
              "│    │    │    └─ResBlock (2)                                [1, 48, 13, 20, 20]       [1, 48, 13, 20, 20]\n",
              "│    │    │    └─ResBlock (3)                                [1, 48, 13, 20, 20]       [1, 48, 13, 20, 20]\n",
              "│    │    │    └─ResBlock (4)                                [1, 48, 13, 20, 20]       [1, 48, 13, 20, 20]\n",
              "│    └─ResStage (3)                                          [1, 48, 13, 20, 20]       [1, 96, 13, 10, 10]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 48, 13, 20, 20]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (1)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (2)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (3)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (4)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (5)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (6)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (7)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (8)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (9)                                [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    │    │    └─ResBlock (10)                               [1, 96, 13, 10, 10]       [1, 96, 13, 10, 10]\n",
              "│    └─ResStage (4)                                          [1, 96, 13, 10, 10]       [1, 192, 13, 5, 5]\n",
              "│    │    └─ModuleList (res_blocks)                          --                        --\n",
              "│    │    │    └─ResBlock (0)                                [1, 96, 13, 10, 10]       [1, 192, 13, 5, 5]\n",
              "│    │    │    └─ResBlock (1)                                [1, 192, 13, 5, 5]        [1, 192, 13, 5, 5]\n",
              "│    │    │    └─ResBlock (2)                                [1, 192, 13, 5, 5]        [1, 192, 13, 5, 5]\n",
              "│    │    │    └─ResBlock (3)                                [1, 192, 13, 5, 5]        [1, 192, 13, 5, 5]\n",
              "│    │    │    └─ResBlock (4)                                [1, 192, 13, 5, 5]        [1, 192, 13, 5, 5]\n",
              "│    │    │    └─ResBlock (5)                                [1, 192, 13, 5, 5]        [1, 192, 13, 5, 5]\n",
              "│    │    │    └─ResBlock (6)                                [1, 192, 13, 5, 5]        [1, 192, 13, 5, 5]\n",
              "│    └─ResNetBasicHead (5)                                   [1, 192, 13, 5, 5]        [1, 400]\n",
              "│    │    └─ProjectedPool (pool)                             [1, 192, 13, 5, 5]        [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─Conv3d (pre_conv)                           [1, 192, 13, 5, 5]        [1, 432, 13, 5, 5]\n",
              "│    │    │    └─BatchNorm3d (pre_norm)                      [1, 432, 13, 5, 5]        [1, 432, 13, 5, 5]\n",
              "│    │    │    └─ReLU (pre_act)                              [1, 432, 13, 5, 5]        [1, 432, 13, 5, 5]\n",
              "│    │    │    └─AvgPool3d (pool)                            [1, 432, 13, 5, 5]        [1, 432, 1, 1, 1]\n",
              "│    │    │    └─Conv3d (post_conv)                          [1, 432, 1, 1, 1]         [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─ReLU (post_act)                             [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Dropout (dropout)                                [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                                    [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─Softmax (activation)                             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)                  [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "==============================================================================================================\n",
              "Total params: 3,794,274\n",
              "Trainable params: 3,794,274\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 1.96\n",
              "==============================================================================================================\n",
              "Input size (MB): 3.99\n",
              "Forward/backward pass size (MB): 563.15\n",
              "Params size (MB): 15.18\n",
              "Estimated Total Size (MB): 582.32\n",
              "=============================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fkGv25WK_53"
      },
      "source": [
        "# SlowFast"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "32fbd99e7537467d9cdc25a8a54d71cb",
            "91c3b2b0f6ca4c5ea11846558407aef3",
            "7abf8419ce8c448fae678e715e3fe21d",
            "6b6ef1b86db04ce4b6f941e178435bd9",
            "deb71142733543e19da1de49a2709a09",
            "a2815fdde9e44f16b6dd40e10f4d1b32",
            "d46a6bce70964222a119339145960f69",
            "c1fbec6568cb466ca17e07f051bd88fa",
            "3b47626c46ac492c8462f830c5f402b5",
            "3894c1e64d67406795811a88e01a5160",
            "2b7a3fcfc6104f67bcd101311588582f"
          ]
        },
        "id": "oNhrfzqrEags",
        "outputId": "15ee425a-bf82-4159-b31d-4e44ae6f58bc"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/SLOWFAST_8x8_R50.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'slowfast_r50', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "slow_frames = 32\n",
        "fast_frames = 8\n",
        "\n",
        "input_data = [[\n",
        "              torch.zeros(batch_size, 3, fast_frames, 224, 224),\n",
        "              torch.zeros(batch_size, 3, slow_frames, 224, 224),\n",
        "              ]]\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_data=input_data,\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/SLOWFAST_8x8_R50.pyth\" to /root/.cache/torch/hub/checkpoints/SLOWFAST_8x8_R50.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32fbd99e7537467d9cdc25a8a54d71cb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/264M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "===================================================================================================================\n",
              "Layer (type (var_name))                                           Input Shape               Output Shape\n",
              "===================================================================================================================\n",
              "Net                                                               --                        --\n",
              "├─ModuleList (blocks)                                             --                        --\n",
              "│    └─MultiPathWayWithFuse (0)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (1)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (2)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (3)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (4)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─PoolConcatPathway (5)                                      --                        --\n",
              "│    │    └─ModuleList (pool)                                     --                        --\n",
              "│    └─MultiPathWayWithFuse (0)                                   [1, 64, 8, 56, 56]        [1, 80, 8, 56, 56]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResNetBasicStem (0)                              [1, 3, 8, 224, 224]       [1, 64, 8, 56, 56]\n",
              "│    │    │    └─ResNetBasicStem (1)                              [1, 3, 32, 224, 224]      [1, 8, 32, 56, 56]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 64, 8, 56, 56]        [1, 80, 8, 56, 56]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 8, 32, 56, 56]        [1, 16, 8, 56, 56]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 16, 8, 56, 56]        [1, 16, 8, 56, 56]\n",
              "│    │    │    └─ReLU (activation)                                [1, 16, 8, 56, 56]        [1, 16, 8, 56, 56]\n",
              "│    └─MultiPathWayWithFuse (1)                                   [1, 256, 8, 56, 56]       [1, 320, 8, 56, 56]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 80, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResStage (1)                                     [1, 8, 32, 56, 56]        [1, 32, 32, 56, 56]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 256, 8, 56, 56]       [1, 320, 8, 56, 56]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 32, 32, 56, 56]       [1, 64, 8, 56, 56]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 64, 8, 56, 56]        [1, 64, 8, 56, 56]\n",
              "│    │    │    └─ReLU (activation)                                [1, 64, 8, 56, 56]        [1, 64, 8, 56, 56]\n",
              "│    └─MultiPathWayWithFuse (2)                                   [1, 512, 8, 28, 28]       [1, 640, 8, 28, 28]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 320, 8, 56, 56]       [1, 512, 8, 28, 28]\n",
              "│    │    │    └─ResStage (1)                                     [1, 32, 32, 56, 56]       [1, 64, 32, 28, 28]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 512, 8, 28, 28]       [1, 640, 8, 28, 28]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 64, 32, 28, 28]       [1, 128, 8, 28, 28]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 128, 8, 28, 28]       [1, 128, 8, 28, 28]\n",
              "│    │    │    └─ReLU (activation)                                [1, 128, 8, 28, 28]       [1, 128, 8, 28, 28]\n",
              "│    └─MultiPathWayWithFuse (3)                                   [1, 1024, 8, 14, 14]      [1, 1280, 8, 14, 14]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 640, 8, 28, 28]       [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResStage (1)                                     [1, 64, 32, 28, 28]       [1, 128, 32, 14, 14]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 1024, 8, 14, 14]      [1, 1280, 8, 14, 14]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 128, 32, 14, 14]      [1, 256, 8, 14, 14]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 256, 8, 14, 14]       [1, 256, 8, 14, 14]\n",
              "│    │    │    └─ReLU (activation)                                [1, 256, 8, 14, 14]       [1, 256, 8, 14, 14]\n",
              "│    └─MultiPathWayWithFuse (4)                                   [1, 2048, 8, 7, 7]        [1, 2048, 8, 7, 7]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 1280, 8, 14, 14]      [1, 2048, 8, 7, 7]\n",
              "│    │    │    └─ResStage (1)                                     [1, 128, 32, 14, 14]      [1, 256, 32, 7, 7]\n",
              "│    │    └─Identity (multipathway_fusion)                        [1, 2048, 8, 7, 7]        [1, 2048, 8, 7, 7]\n",
              "│    └─PoolConcatPathway (5)                                      [1, 2048, 1, 1, 1]        [1, 2304, 1, 1, 1]\n",
              "│    │    └─ModuleList (pool)                                     --                        --\n",
              "│    │    │    └─AvgPool3d (0)                                    [1, 2048, 8, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─AvgPool3d (1)                                    [1, 256, 32, 7, 7]        [1, 256, 1, 1, 1]\n",
              "│    └─ResNetBasicHead (6)                                        [1, 2304, 1, 1, 1]        [1, 400]\n",
              "│    │    └─Dropout (dropout)                                     [1, 2304, 1, 1, 1]        [1, 2304, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                                         [1, 1, 1, 1, 2304]        [1, 1, 1, 1, 400]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)                       [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "===================================================================================================================\n",
              "Total params: 34,566,488\n",
              "Trainable params: 34,566,488\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 50.31\n",
              "===================================================================================================================\n",
              "Input size (MB): 9.63\n",
              "Forward/backward pass size (MB): 2185.27\n",
              "Params size (MB): 138.27\n",
              "Estimated Total Size (MB): 2333.17\n",
              "==================================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "f48f9d933a364dfb8fb6ffd0508cf752",
            "f0e28a94dd3947969b413cf70fe1b3cf",
            "00f34d5243884b779321632ff2dd25b5",
            "21ecca8b6094464191704bdeaee4b02b",
            "a4436aac7b4a4be7b1162bfc7b2e7021",
            "2e51fc9dd80546efbb1c24227a81d421",
            "be6ccfd1a1f84ba185f8ee26da880c34",
            "57d052cf9c3046509922360967b885fc",
            "14c0fec9e5c940fb9659588508a97b85",
            "e2ae7cfc66b64475b80e5cffa3178f52",
            "f8dd6dab273644ffb8da6c4348075ab7"
          ]
        },
        "id": "b3lSbCY7FHEX",
        "outputId": "39b22a00-e47f-4392-9562-e7c305753984"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/SLOWFAST_8x8_R101.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'slowfast_r101', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "slow_frames = 32\n",
        "fast_frames = 8\n",
        "\n",
        "input_data = [[\n",
        "              torch.zeros(batch_size, 3, fast_frames, 224, 224),\n",
        "              torch.zeros(batch_size, 3, slow_frames, 224, 224),\n",
        "              ]]\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_data=input_data,\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/SLOWFAST_8x8_R101.pyth\" to /root/.cache/torch/hub/checkpoints/SLOWFAST_8x8_R101.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f48f9d933a364dfb8fb6ffd0508cf752",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/480M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "===================================================================================================================\n",
              "Layer (type (var_name))                                           Input Shape               Output Shape\n",
              "===================================================================================================================\n",
              "Net                                                               --                        --\n",
              "├─ModuleList (blocks)                                             --                        --\n",
              "│    └─MultiPathWayWithFuse (0)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (1)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (2)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (3)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (4)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─PoolConcatPathway (5)                                      --                        --\n",
              "│    │    └─ModuleList (pool)                                     --                        --\n",
              "│    └─MultiPathWayWithFuse (0)                                   [1, 64, 8, 56, 56]        [1, 80, 8, 56, 56]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResNetBasicStem (0)                              [1, 3, 8, 224, 224]       [1, 64, 8, 56, 56]\n",
              "│    │    │    └─ResNetBasicStem (1)                              [1, 3, 32, 224, 224]      [1, 8, 32, 56, 56]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 64, 8, 56, 56]        [1, 80, 8, 56, 56]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 8, 32, 56, 56]        [1, 16, 8, 56, 56]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 16, 8, 56, 56]        [1, 16, 8, 56, 56]\n",
              "│    │    │    └─ReLU (activation)                                [1, 16, 8, 56, 56]        [1, 16, 8, 56, 56]\n",
              "│    └─MultiPathWayWithFuse (1)                                   [1, 256, 8, 56, 56]       [1, 320, 8, 56, 56]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 80, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResStage (1)                                     [1, 8, 32, 56, 56]        [1, 32, 32, 56, 56]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 256, 8, 56, 56]       [1, 320, 8, 56, 56]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 32, 32, 56, 56]       [1, 64, 8, 56, 56]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 64, 8, 56, 56]        [1, 64, 8, 56, 56]\n",
              "│    │    │    └─ReLU (activation)                                [1, 64, 8, 56, 56]        [1, 64, 8, 56, 56]\n",
              "│    └─MultiPathWayWithFuse (2)                                   [1, 512, 8, 28, 28]       [1, 640, 8, 28, 28]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 320, 8, 56, 56]       [1, 512, 8, 28, 28]\n",
              "│    │    │    └─ResStage (1)                                     [1, 32, 32, 56, 56]       [1, 64, 32, 28, 28]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 512, 8, 28, 28]       [1, 640, 8, 28, 28]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 64, 32, 28, 28]       [1, 128, 8, 28, 28]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 128, 8, 28, 28]       [1, 128, 8, 28, 28]\n",
              "│    │    │    └─ReLU (activation)                                [1, 128, 8, 28, 28]       [1, 128, 8, 28, 28]\n",
              "│    └─MultiPathWayWithFuse (3)                                   [1, 1024, 8, 14, 14]      [1, 1280, 8, 14, 14]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 640, 8, 28, 28]       [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResStage (1)                                     [1, 64, 32, 28, 28]       [1, 128, 32, 14, 14]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 1024, 8, 14, 14]      [1, 1280, 8, 14, 14]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 128, 32, 14, 14]      [1, 256, 8, 14, 14]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 256, 8, 14, 14]       [1, 256, 8, 14, 14]\n",
              "│    │    │    └─ReLU (activation)                                [1, 256, 8, 14, 14]       [1, 256, 8, 14, 14]\n",
              "│    └─MultiPathWayWithFuse (4)                                   [1, 2048, 8, 7, 7]        [1, 2048, 8, 7, 7]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 1280, 8, 14, 14]      [1, 2048, 8, 7, 7]\n",
              "│    │    │    └─ResStage (1)                                     [1, 128, 32, 14, 14]      [1, 256, 32, 7, 7]\n",
              "│    │    └─Identity (multipathway_fusion)                        [1, 2048, 8, 7, 7]        [1, 2048, 8, 7, 7]\n",
              "│    └─PoolConcatPathway (5)                                      [1, 2048, 1, 1, 1]        [1, 2304, 1, 1, 1]\n",
              "│    │    └─ModuleList (pool)                                     --                        --\n",
              "│    │    │    └─AvgPool3d (0)                                    [1, 2048, 8, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─AvgPool3d (1)                                    [1, 256, 32, 7, 7]        [1, 256, 1, 1, 1]\n",
              "│    └─ResNetBasicHead (6)                                        [1, 2304, 1, 1, 1]        [1, 400]\n",
              "│    │    └─Dropout (dropout)                                     [1, 2304, 1, 1, 1]        [1, 2304, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                                         [1, 1, 1, 1, 2304]        [1, 1, 1, 1, 400]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)                       [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "===================================================================================================================\n",
              "Total params: 62,826,968\n",
              "Trainable params: 62,826,968\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 96.40\n",
              "===================================================================================================================\n",
              "Input size (MB): 9.63\n",
              "Forward/backward pass size (MB): 3167.92\n",
              "Params size (MB): 251.31\n",
              "Estimated Total Size (MB): 3428.86\n",
              "==================================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "12a746c6d2a14953b260fc3834e137c1",
            "670a7c1a3e794e6f9d38d2ac3e70cd82",
            "91ef61ae9d9a4e7899b2fb61b70cd06a",
            "77da28b7b4ce45f8b82ceda992f41c9a",
            "83b99d81a8f5432ab964a2f09e569623",
            "44463ee6a2eb4ef8954f4b979ab5152c",
            "3b93c148af514d8ebca5cb1eee2e346c",
            "2dd42ab26a0c4604a2e038da74722146",
            "be7b3cb9640c49b7a33403c9e1ad25a5",
            "d9fe09ddfb294d5e9738fbd3c599c7eb",
            "935486f081644e90994a5f60f0425b19"
          ]
        },
        "id": "WQxKVBO2LFxS",
        "outputId": "00643f9f-89eb-49b8-b794-3818143df5de"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/SLOWFAST_16x8_R101_50_50.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'slowfast_16x8_r101_50_50', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "slow_frames = 64\n",
        "fast_frames = 16\n",
        "\n",
        "input_data = [[\n",
        "              torch.zeros(batch_size, 3, fast_frames, 224, 224),\n",
        "              torch.zeros(batch_size, 3, slow_frames, 224, 224),\n",
        "              ]]\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_data=input_data,\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/SLOWFAST_16x8_R101_50_50.pyth\" to /root/.cache/torch/hub/checkpoints/SLOWFAST_16x8_R101_50_50.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "12a746c6d2a14953b260fc3834e137c1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/411M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "===================================================================================================================\n",
              "Layer (type (var_name))                                           Input Shape               Output Shape\n",
              "===================================================================================================================\n",
              "Net                                                               --                        --\n",
              "├─ModuleList (blocks)                                             --                        --\n",
              "│    └─MultiPathWayWithFuse (0)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (1)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (2)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (3)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─MultiPathWayWithFuse (4)                                   --                        --\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    └─PoolConcatPathway (5)                                      --                        --\n",
              "│    │    └─ModuleList (pool)                                     --                        --\n",
              "│    └─MultiPathWayWithFuse (0)                                   [1, 64, 16, 56, 56]       [1, 80, 16, 56, 56]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResNetBasicStem (0)                              [1, 3, 16, 224, 224]      [1, 64, 16, 56, 56]\n",
              "│    │    │    └─ResNetBasicStem (1)                              [1, 3, 64, 224, 224]      [1, 8, 64, 56, 56]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 64, 16, 56, 56]       [1, 80, 16, 56, 56]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 8, 64, 56, 56]        [1, 16, 16, 56, 56]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 16, 16, 56, 56]       [1, 16, 16, 56, 56]\n",
              "│    │    │    └─ReLU (activation)                                [1, 16, 16, 56, 56]       [1, 16, 16, 56, 56]\n",
              "│    └─MultiPathWayWithFuse (1)                                   [1, 256, 16, 56, 56]      [1, 320, 16, 56, 56]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 80, 16, 56, 56]       [1, 256, 16, 56, 56]\n",
              "│    │    │    └─ResStage (1)                                     [1, 8, 64, 56, 56]        [1, 32, 64, 56, 56]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 256, 16, 56, 56]      [1, 320, 16, 56, 56]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 32, 64, 56, 56]       [1, 64, 16, 56, 56]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 64, 16, 56, 56]       [1, 64, 16, 56, 56]\n",
              "│    │    │    └─ReLU (activation)                                [1, 64, 16, 56, 56]       [1, 64, 16, 56, 56]\n",
              "│    └─MultiPathWayWithFuse (2)                                   [1, 512, 16, 28, 28]      [1, 640, 16, 28, 28]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 320, 16, 56, 56]      [1, 512, 16, 28, 28]\n",
              "│    │    │    └─ResStage (1)                                     [1, 32, 64, 56, 56]       [1, 64, 64, 28, 28]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 512, 16, 28, 28]      [1, 640, 16, 28, 28]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 64, 64, 28, 28]       [1, 128, 16, 28, 28]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 128, 16, 28, 28]      [1, 128, 16, 28, 28]\n",
              "│    │    │    └─ReLU (activation)                                [1, 128, 16, 28, 28]      [1, 128, 16, 28, 28]\n",
              "│    └─MultiPathWayWithFuse (3)                                   [1, 1024, 16, 14, 14]     [1, 1280, 16, 14, 14]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 640, 16, 28, 28]      [1, 1024, 16, 14, 14]\n",
              "│    │    │    └─ResStage (1)                                     [1, 64, 64, 28, 28]       [1, 128, 64, 14, 14]\n",
              "│    │    └─FuseFastToSlow (multipathway_fusion)                  [1, 1024, 16, 14, 14]     [1, 1280, 16, 14, 14]\n",
              "│    │    │    └─Conv3d (conv_fast_to_slow)                       [1, 128, 64, 14, 14]      [1, 256, 16, 14, 14]\n",
              "│    │    │    └─BatchNorm3d (norm)                               [1, 256, 16, 14, 14]      [1, 256, 16, 14, 14]\n",
              "│    │    │    └─ReLU (activation)                                [1, 256, 16, 14, 14]      [1, 256, 16, 14, 14]\n",
              "│    └─MultiPathWayWithFuse (4)                                   [1, 2048, 16, 7, 7]       [1, 2048, 16, 7, 7]\n",
              "│    │    └─ModuleList (multipathway_blocks)                      --                        --\n",
              "│    │    │    └─ResStage (0)                                     [1, 1280, 16, 14, 14]     [1, 2048, 16, 7, 7]\n",
              "│    │    │    └─ResStage (1)                                     [1, 128, 64, 14, 14]      [1, 256, 64, 7, 7]\n",
              "│    │    └─Identity (multipathway_fusion)                        [1, 2048, 16, 7, 7]       [1, 2048, 16, 7, 7]\n",
              "│    └─PoolConcatPathway (5)                                      [1, 2048, 1, 1, 1]        [1, 2304, 1, 1, 1]\n",
              "│    │    └─ModuleList (pool)                                     --                        --\n",
              "│    │    │    └─AvgPool3d (0)                                    [1, 2048, 16, 7, 7]       [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─AvgPool3d (1)                                    [1, 256, 64, 7, 7]        [1, 256, 1, 1, 1]\n",
              "│    └─ResNetBasicHead (6)                                        [1, 2304, 1, 1, 1]        [1, 400]\n",
              "│    │    └─Dropout (dropout)                                     [1, 2304, 1, 1, 1]        [1, 2304, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                                         [1, 1, 1, 1, 2304]        [1, 1, 1, 1, 400]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)                       [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "===================================================================================================================\n",
              "Total params: 53,774,808\n",
              "Trainable params: 53,774,808\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 163.09\n",
              "===================================================================================================================\n",
              "Input size (MB): 19.27\n",
              "Forward/backward pass size (MB): 6335.83\n",
              "Params size (MB): 215.10\n",
              "Estimated Total Size (MB): 6570.19\n",
              "==================================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSKXbmt_TNtM"
      },
      "source": [
        "# # https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/SLOWFAST_4x16_R50.yaml\n",
        "# torch.hub.load_state_dict_from_url('https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/SLOWFAST_4x16_R50.pyth')\n",
        "# model = torch.load('/root/.cache/torch/hub/checkpoints/SLOWFAST_4x16_R50.pyth')\n",
        "\n",
        "# batch_size = 1\n",
        "# fast_frames = 32\n",
        "# slow_frames = 8\n",
        "\n",
        "# input_data = [[\n",
        "#               torch.zeros(batch_size, 3, slow_frames, 224, 224),\n",
        "#               torch.zeros(batch_size, 3, fast_frames, 224, 224),\n",
        "#               ]]\n",
        "# torchinfo.summary(\n",
        "#     model=model,\n",
        "#     input_data=input_data,\n",
        "#     depth=4,\n",
        "#     col_names=[\"input_size\",\n",
        "#                \"output_size\"],\n",
        "#     row_settings=(\"var_names\",)\n",
        "# )"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GSwnBUtWTiO",
        "outputId": "f8d6a05d-6e1b-4714-bcb6-89242fe7554e"
      },
      "source": [
        "!ls -l /root/.cache/torch/hub/checkpoints/"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 1292808\n",
            "-rw------- 1 root root 431301345 Aug 29 23:19 SLOWFAST_16x8_R101_50_50.pyth\n",
            "-rw------- 1 root root 503790111 Aug 29 23:18 SLOWFAST_8x8_R101.pyth\n",
            "-rw------- 1 root root 277138115 Aug 29 23:18 SLOWFAST_8x8_R50.pyth\n",
            "-rw------- 1 root root  50025453 Aug 29 23:17 X3D_L.pyth\n",
            "-rw------- 1 root root  30779313 Aug 29 23:17 X3D_M.pyth\n",
            "-rw------- 1 root root  30779313 Aug 29 23:17 X3D_S.pyth\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKVrV3j1PUWd"
      },
      "source": [
        "# Misc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "ce9ad7ca5755453397f245fc5d71e25b",
            "a19aa113e8e54fb4935222c1b8ba244e",
            "ee622d52bc5d4efe8b0c398043d551bd",
            "676aab7651bc420a84156a8384691052",
            "2ceb01db267a4ddbaa63411d58e63789",
            "e2b1994c4ee14616acbff375049dcafe",
            "b1632841f72c493c9e828d4d09a34aee",
            "c67b196684d94f2a9472a0b267976563",
            "f2c14c32514a40faa73cb8a99d874d81",
            "54428776dbaf4764a8b63f121ff22558",
            "4ce5af13be4a429399ca0d7412160a34"
          ]
        },
        "id": "p7XRADsuLUhh",
        "outputId": "b9825c25-ba79-490b-ac38-59f7d497091f"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/I3D_8x8_R50.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'i3d_r50', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 8\n",
        "size = 224\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, size, size),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/I3D_8x8_R50.pyth\" to /root/.cache/torch/hub/checkpoints/I3D_8x8_R50.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce9ad7ca5755453397f245fc5d71e25b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/214M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "=========================================================================================================\n",
              "Layer (type (var_name))                                 Input Shape               Output Shape\n",
              "=========================================================================================================\n",
              "Net                                                     --                        --\n",
              "├─ModuleList (blocks)                                   --                        --\n",
              "│    └─ResStage (1)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (3)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (4)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (5)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResNetBasicStem (0)                              [1, 3, 8, 224, 224]       [1, 64, 8, 56, 56]\n",
              "│    │    └─Conv3d (conv)                               [1, 3, 8, 224, 224]       [1, 64, 8, 112, 112]\n",
              "│    │    └─BatchNorm3d (norm)                          [1, 64, 8, 112, 112]      [1, 64, 8, 112, 112]\n",
              "│    │    └─ReLU (activation)                           [1, 64, 8, 112, 112]      [1, 64, 8, 112, 112]\n",
              "│    │    └─MaxPool3d (pool)                            [1, 64, 8, 112, 112]      [1, 64, 8, 56, 56]\n",
              "│    └─ResStage (1)                                     [1, 64, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 64, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResBlock (1)                           [1, 256, 8, 56, 56]       [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResBlock (2)                           [1, 256, 8, 56, 56]       [1, 256, 8, 56, 56]\n",
              "│    └─MaxPool3d (2)                                    [1, 256, 8, 56, 56]       [1, 256, 4, 56, 56]\n",
              "│    └─ResStage (3)                                     [1, 256, 4, 56, 56]       [1, 512, 4, 28, 28]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 256, 4, 56, 56]       [1, 512, 4, 28, 28]\n",
              "│    │    │    └─ResBlock (1)                           [1, 512, 4, 28, 28]       [1, 512, 4, 28, 28]\n",
              "│    │    │    └─ResBlock (2)                           [1, 512, 4, 28, 28]       [1, 512, 4, 28, 28]\n",
              "│    │    │    └─ResBlock (3)                           [1, 512, 4, 28, 28]       [1, 512, 4, 28, 28]\n",
              "│    └─ResStage (4)                                     [1, 512, 4, 28, 28]       [1, 1024, 4, 14, 14]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 512, 4, 28, 28]       [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (1)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (2)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (3)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (4)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (5)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    └─ResStage (5)                                     [1, 1024, 4, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 1024, 4, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (1)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (2)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    └─ResNetBasicHead (6)                              [1, 2048, 4, 7, 7]        [1, 400]\n",
              "│    │    └─AvgPool3d (pool)                            [1, 2048, 4, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Dropout (dropout)                           [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                               [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "=========================================================================================================\n",
              "Total params: 28,043,472\n",
              "Trainable params: 28,043,472\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 28.41\n",
              "=========================================================================================================\n",
              "Input size (MB): 4.82\n",
              "Forward/backward pass size (MB): 1045.27\n",
              "Params size (MB): 112.17\n",
              "Estimated Total Size (MB): 1162.26\n",
              "========================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "1dba3656be5c4e48b51da46bca2203b1",
            "1b7cb618a71844e29655081b83f728be",
            "d5bfc489b4ff4759b4c7a2ac01f4d3b1",
            "bb2aff406f7c431ab8d4e0022f0e67a1",
            "76bf533318954600bf2df053db7d1ef0",
            "51e952a159cf4763aae6868f3fe279d5",
            "36842fd7f71544cf8b0b8a3f458f456c",
            "672a216b3481455290ca6e33639a1e8f",
            "a74b75034d9b40c6a631f58ed7c1f1a2",
            "5fe2f3bc84924c6bbe5575f39d8c330c",
            "0c3f626df245428b811201eb422cb836"
          ]
        },
        "id": "xzxTGtLYPcEq",
        "outputId": "dfb686af-9353-4cf1-cc24-54260e1f8da3"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/SLOW_8x8_R50.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'slow_r50', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 8\n",
        "size = 224\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, size, size),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/SLOW_8x8_R50.pyth\" to /root/.cache/torch/hub/checkpoints/SLOW_8x8_R50.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dba3656be5c4e48b51da46bca2203b1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/248M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "=========================================================================================================\n",
              "Layer (type (var_name))                                 Input Shape               Output Shape\n",
              "=========================================================================================================\n",
              "Net                                                     --                        --\n",
              "├─ModuleList (blocks)                                   --                        --\n",
              "│    └─ResStage (1)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (2)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (3)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (4)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResNetBasicStem (0)                              [1, 3, 8, 224, 224]       [1, 64, 8, 56, 56]\n",
              "│    │    └─Conv3d (conv)                               [1, 3, 8, 224, 224]       [1, 64, 8, 112, 112]\n",
              "│    │    └─BatchNorm3d (norm)                          [1, 64, 8, 112, 112]      [1, 64, 8, 112, 112]\n",
              "│    │    └─ReLU (activation)                           [1, 64, 8, 112, 112]      [1, 64, 8, 112, 112]\n",
              "│    │    └─MaxPool3d (pool)                            [1, 64, 8, 112, 112]      [1, 64, 8, 56, 56]\n",
              "│    └─ResStage (1)                                     [1, 64, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 64, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResBlock (1)                           [1, 256, 8, 56, 56]       [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResBlock (2)                           [1, 256, 8, 56, 56]       [1, 256, 8, 56, 56]\n",
              "│    └─ResStage (2)                                     [1, 256, 8, 56, 56]       [1, 512, 8, 28, 28]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 256, 8, 56, 56]       [1, 512, 8, 28, 28]\n",
              "│    │    │    └─ResBlock (1)                           [1, 512, 8, 28, 28]       [1, 512, 8, 28, 28]\n",
              "│    │    │    └─ResBlock (2)                           [1, 512, 8, 28, 28]       [1, 512, 8, 28, 28]\n",
              "│    │    │    └─ResBlock (3)                           [1, 512, 8, 28, 28]       [1, 512, 8, 28, 28]\n",
              "│    └─ResStage (3)                                     [1, 512, 8, 28, 28]       [1, 1024, 8, 14, 14]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 512, 8, 28, 28]       [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (1)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (2)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (3)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (4)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (5)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    └─ResStage (4)                                     [1, 1024, 8, 14, 14]      [1, 2048, 8, 7, 7]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 1024, 8, 14, 14]      [1, 2048, 8, 7, 7]\n",
              "│    │    │    └─ResBlock (1)                           [1, 2048, 8, 7, 7]        [1, 2048, 8, 7, 7]\n",
              "│    │    │    └─ResBlock (2)                           [1, 2048, 8, 7, 7]        [1, 2048, 8, 7, 7]\n",
              "│    └─ResNetBasicHead (5)                              [1, 2048, 8, 7, 7]        [1, 400]\n",
              "│    │    └─AvgPool3d (pool)                            [1, 2048, 8, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Dropout (dropout)                           [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                               [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "=========================================================================================================\n",
              "Total params: 32,454,096\n",
              "Trainable params: 32,454,096\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 41.74\n",
              "=========================================================================================================\n",
              "Input size (MB): 4.82\n",
              "Forward/backward pass size (MB): 1422.59\n",
              "Params size (MB): 129.82\n",
              "Estimated Total Size (MB): 1557.23\n",
              "========================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c133762aef64470384b822f4af7ddc33",
            "4b9e5eb964fc4d3b9dcb2939ed1f0d11",
            "bca2b74de6dd4195be7379f004bcb64f",
            "91720905fe4248d78965fbc245d6b352",
            "8bd152351d52440d86628592a4abcfe1",
            "c7023fa712794c0bacf9935520a60790",
            "bba6c33568c649d4a75d01d204e2369e",
            "c82885e3424845b58585ca37a4ac01db",
            "04964313892b4a63897722ceae9db31c",
            "73ed3bf519344e1596898eb4de8a60dc",
            "4bc90f67ee05425bb21a81cb8a3f06c8"
          ]
        },
        "id": "P7NpNpTcPi4l",
        "outputId": "af2ffbc9-263e-4007-e3b7-93a4735e953b"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/C2D_8x8_R50.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'c2d_r50', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 8\n",
        "size = 224\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, size, size),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/C2D_8x8_R50.pyth\" to /root/.cache/torch/hub/checkpoints/C2D_8x8_R50.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c133762aef64470384b822f4af7ddc33",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/186M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "=========================================================================================================\n",
              "Layer (type (var_name))                                 Input Shape               Output Shape\n",
              "=========================================================================================================\n",
              "Net                                                     --                        --\n",
              "├─ModuleList (blocks)                                   --                        --\n",
              "│    └─ResStage (1)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (3)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (4)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (5)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResNetBasicStem (0)                              [1, 3, 8, 224, 224]       [1, 64, 8, 56, 56]\n",
              "│    │    └─Conv3d (conv)                               [1, 3, 8, 224, 224]       [1, 64, 8, 112, 112]\n",
              "│    │    └─BatchNorm3d (norm)                          [1, 64, 8, 112, 112]      [1, 64, 8, 112, 112]\n",
              "│    │    └─ReLU (activation)                           [1, 64, 8, 112, 112]      [1, 64, 8, 112, 112]\n",
              "│    │    └─MaxPool3d (pool)                            [1, 64, 8, 112, 112]      [1, 64, 8, 56, 56]\n",
              "│    └─ResStage (1)                                     [1, 64, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 64, 8, 56, 56]        [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResBlock (1)                           [1, 256, 8, 56, 56]       [1, 256, 8, 56, 56]\n",
              "│    │    │    └─ResBlock (2)                           [1, 256, 8, 56, 56]       [1, 256, 8, 56, 56]\n",
              "│    └─MaxPool3d (2)                                    [1, 256, 8, 56, 56]       [1, 256, 4, 56, 56]\n",
              "│    └─ResStage (3)                                     [1, 256, 4, 56, 56]       [1, 512, 4, 28, 28]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 256, 4, 56, 56]       [1, 512, 4, 28, 28]\n",
              "│    │    │    └─ResBlock (1)                           [1, 512, 4, 28, 28]       [1, 512, 4, 28, 28]\n",
              "│    │    │    └─ResBlock (2)                           [1, 512, 4, 28, 28]       [1, 512, 4, 28, 28]\n",
              "│    │    │    └─ResBlock (3)                           [1, 512, 4, 28, 28]       [1, 512, 4, 28, 28]\n",
              "│    └─ResStage (4)                                     [1, 512, 4, 28, 28]       [1, 1024, 4, 14, 14]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 512, 4, 28, 28]       [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (1)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (2)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (3)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (4)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    │    │    └─ResBlock (5)                           [1, 1024, 4, 14, 14]      [1, 1024, 4, 14, 14]\n",
              "│    └─ResStage (5)                                     [1, 1024, 4, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 1024, 4, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (1)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (2)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    └─ResNetBasicHead (6)                              [1, 2048, 4, 7, 7]        [1, 400]\n",
              "│    │    └─AvgPool3d (pool)                            [1, 2048, 4, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Dropout (dropout)                           [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                               [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "=========================================================================================================\n",
              "Total params: 24,327,632\n",
              "Trainable params: 24,327,632\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 19.49\n",
              "=========================================================================================================\n",
              "Input size (MB): 4.82\n",
              "Forward/backward pass size (MB): 1045.27\n",
              "Params size (MB): 97.31\n",
              "Estimated Total Size (MB): 1147.40\n",
              "========================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "89a12b6444e34d66bb2c86de2b5b1259",
            "14f2bf6291fe4f89a2700da2de3667ec",
            "15a59476582942eaaae76b173bc74bda",
            "9d41412d80c542159d02ea2b3fa0fe44",
            "00cf167e717a4e9da13769d0e085889b",
            "26d297406c1e41afa2bbd987b659dbdb",
            "a80326c8b07a4ea488bebf2f7dab1d9a",
            "b25a3c0c63b44827baab41a35259f1a8",
            "aba8f32053324d5b97dcc62c2ea98ba2",
            "971d4c0462a342b5926910c9f03410e2",
            "74e8f235dfc14c90af8dce652d211426"
          ]
        },
        "id": "wOkIjPCZPqd_",
        "outputId": "64fc48c8-babc-46f2-df09-d09246548303"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/CSN_32x2_R101.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'csn_r101', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 32\n",
        "size = 224\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, size, size),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/CSN_32x2_R101.pyth\" to /root/.cache/torch/hub/checkpoints/CSN_32x2_R101.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89a12b6444e34d66bb2c86de2b5b1259",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/170M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "=========================================================================================================\n",
              "Layer (type (var_name))                                 Input Shape               Output Shape\n",
              "=========================================================================================================\n",
              "Net                                                     --                        --\n",
              "├─ModuleList (blocks)                                   --                        --\n",
              "│    └─ResStage (1)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (2)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (3)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (4)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResNetBasicStem (0)                              [1, 3, 32, 224, 224]      [1, 64, 32, 56, 56]\n",
              "│    │    └─Conv3d (conv)                               [1, 3, 32, 224, 224]      [1, 64, 32, 112, 112]\n",
              "│    │    └─BatchNorm3d (norm)                          [1, 64, 32, 112, 112]     [1, 64, 32, 112, 112]\n",
              "│    │    └─ReLU (activation)                           [1, 64, 32, 112, 112]     [1, 64, 32, 112, 112]\n",
              "│    │    └─MaxPool3d (pool)                            [1, 64, 32, 112, 112]     [1, 64, 32, 56, 56]\n",
              "│    └─ResStage (1)                                     [1, 64, 32, 56, 56]       [1, 256, 32, 56, 56]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 64, 32, 56, 56]       [1, 256, 32, 56, 56]\n",
              "│    │    │    └─ResBlock (1)                           [1, 256, 32, 56, 56]      [1, 256, 32, 56, 56]\n",
              "│    │    │    └─ResBlock (2)                           [1, 256, 32, 56, 56]      [1, 256, 32, 56, 56]\n",
              "│    └─ResStage (2)                                     [1, 256, 32, 56, 56]      [1, 512, 16, 28, 28]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 256, 32, 56, 56]      [1, 512, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (1)                           [1, 512, 16, 28, 28]      [1, 512, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (2)                           [1, 512, 16, 28, 28]      [1, 512, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (3)                           [1, 512, 16, 28, 28]      [1, 512, 16, 28, 28]\n",
              "│    └─ResStage (3)                                     [1, 512, 16, 28, 28]      [1, 1024, 8, 14, 14]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 512, 16, 28, 28]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (1)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (2)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (3)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (4)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (5)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (6)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (7)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (8)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (9)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (10)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (11)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (12)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (13)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (14)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (15)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (16)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (17)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (18)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (19)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (20)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (21)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (22)                          [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    └─ResStage (4)                                     [1, 1024, 8, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 1024, 8, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (1)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (2)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    └─ResNetBasicHead (5)                              [1, 2048, 4, 7, 7]        [1, 400]\n",
              "│    │    └─AvgPool3d (pool)                            [1, 2048, 4, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                               [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "=========================================================================================================\n",
              "Total params: 22,213,776\n",
              "Trainable params: 22,213,776\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 56.47\n",
              "=========================================================================================================\n",
              "Input size (MB): 19.27\n",
              "Forward/backward pass size (MB): 4574.45\n",
              "Params size (MB): 88.86\n",
              "Estimated Total Size (MB): 4682.57\n",
              "========================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "473291c30437490d83e4d98432145838",
            "2a3260f949ae4d16a0904d074a3f3e4f",
            "aaf93682458e4635a7b6e0666bf969fc",
            "6b4e62ec6dd34e8cab3a6a0ab21697f6",
            "2724436c8075461a9a1b047a87e9b028",
            "64b2e2d7999748a5a665c66b9f9618a0",
            "405fcdd5f5ab47f9a99710fa9a807467",
            "29350d0c858540d4b25f9c3f7c7f224d",
            "dc68478d026b48edb5f58ac625e63025",
            "788367f55df94861a3c38c942bb68e0c",
            "4b359f94c2d94d008daed847e978427a"
          ]
        },
        "id": "pXNbGm4xPyr6",
        "outputId": "7ccf8c3e-186b-4369-c98f-c8922e2b99cd"
      },
      "source": [
        "# https://github.com/facebookresearch/SlowFast/blob/master/configs/Kinetics/pytorchvideo/R2PLUS1D_16x4_R50.yaml\n",
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'r2plus1d_r50', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 16\n",
        "size = 224\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, size, size),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/R2PLUS1D_16x4_R50.pyth\" to /root/.cache/torch/hub/checkpoints/R2PLUS1D_16x4_R50.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "473291c30437490d83e4d98432145838",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/215M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "=========================================================================================================\n",
              "Layer (type (var_name))                                 Input Shape               Output Shape\n",
              "=========================================================================================================\n",
              "Net                                                     --                        --\n",
              "├─ModuleList (blocks)                                   --                        --\n",
              "│    └─ResStage (1)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (2)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (3)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResStage (4)                                     --                        --\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    └─ResNetBasicStem (0)                              [1, 3, 16, 224, 224]      [1, 64, 16, 112, 112]\n",
              "│    │    └─Conv3d (conv)                               [1, 3, 16, 224, 224]      [1, 64, 16, 112, 112]\n",
              "│    │    └─BatchNorm3d (norm)                          [1, 64, 16, 112, 112]     [1, 64, 16, 112, 112]\n",
              "│    │    └─ReLU (activation)                           [1, 64, 16, 112, 112]     [1, 64, 16, 112, 112]\n",
              "│    └─ResStage (1)                                     [1, 64, 16, 112, 112]     [1, 256, 16, 56, 56]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 64, 16, 112, 112]     [1, 256, 16, 56, 56]\n",
              "│    │    │    └─ResBlock (1)                           [1, 256, 16, 56, 56]      [1, 256, 16, 56, 56]\n",
              "│    │    │    └─ResBlock (2)                           [1, 256, 16, 56, 56]      [1, 256, 16, 56, 56]\n",
              "│    └─ResStage (2)                                     [1, 256, 16, 56, 56]      [1, 512, 16, 28, 28]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 256, 16, 56, 56]      [1, 512, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (1)                           [1, 512, 16, 28, 28]      [1, 512, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (2)                           [1, 512, 16, 28, 28]      [1, 512, 16, 28, 28]\n",
              "│    │    │    └─ResBlock (3)                           [1, 512, 16, 28, 28]      [1, 512, 16, 28, 28]\n",
              "│    └─ResStage (3)                                     [1, 512, 16, 28, 28]      [1, 1024, 8, 14, 14]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 512, 16, 28, 28]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (1)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (2)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (3)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (4)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    │    │    └─ResBlock (5)                           [1, 1024, 8, 14, 14]      [1, 1024, 8, 14, 14]\n",
              "│    └─ResStage (4)                                     [1, 1024, 8, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    └─ModuleList (res_blocks)                     --                        --\n",
              "│    │    │    └─ResBlock (0)                           [1, 1024, 8, 14, 14]      [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (1)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    │    │    └─ResBlock (2)                           [1, 2048, 4, 7, 7]        [1, 2048, 4, 7, 7]\n",
              "│    └─ResNetBasicHead (5)                              [1, 2048, 4, 7, 7]        [1, 400]\n",
              "│    │    └─AvgPool3d (pool)                            [1, 2048, 4, 7, 7]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Dropout (dropout)                           [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "│    │    └─Linear (proj)                               [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    │    └─Softmax (activation)                        [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "│    │    └─AdaptiveAvgPool3d (output_pool)             [1, 400, 1, 1, 1]         [1, 400, 1, 1, 1]\n",
              "=========================================================================================================\n",
              "Total params: 28,107,600\n",
              "Trainable params: 28,107,600\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 57.53\n",
              "=========================================================================================================\n",
              "Input size (MB): 9.63\n",
              "Forward/backward pass size (MB): 3190.39\n",
              "Params size (MB): 112.43\n",
              "Estimated Total Size (MB): 3312.46\n",
              "========================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dixOfiR7P1NV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "a3cd7babd9dc47249c92597a0c392d34",
            "965d47066f9143848664632a8b636f2c",
            "5ad2eb6477cb48c899ffbb8296a1a704",
            "e76ad4fba2d343d9af7b3a008f970901",
            "060da0bf3bcc4943a6b449e4cc8aa9d4",
            "dd842cb83e394ffdb7c898d646a323a7",
            "f76bc25587b54308abcd8fe3a2fc3715",
            "f1fed5f2523e4dba86a81c9e598fffb0",
            "69800b5ed5594d65a3d318926b39e030",
            "2cd5c60de70f4f3fa55367b63bdfd62b",
            "f2d763d00a98457b9dd6717babbe6986"
          ]
        },
        "outputId": "bafbf84e-425d-44b3-f3c4-f91dd9496fc1"
      },
      "source": [
        "model = torch.hub.load('facebookresearch/pytorchvideo', 'efficient_x3d_s', pretrained=True)\n",
        "\n",
        "batch_size = 1\n",
        "frames = 8\n",
        "size = 224\n",
        "\n",
        "torchinfo.summary(\n",
        "    model=model,\n",
        "    input_size=(batch_size, 3, frames, size, size),\n",
        "    depth=4,\n",
        "    col_names=[\"input_size\",\n",
        "               \"output_size\"],\n",
        "    row_settings=(\"var_names\",)\n",
        ")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_master\n",
            "Downloading: \"https://dl.fbaipublicfiles.com/pytorchvideo/model_zoo/kinetics/efficient_x3d_s_original_form.pyth\" to /root/.cache/torch/hub/checkpoints/efficient_x3d_s_original_form.pyth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3cd7babd9dc47249c92597a0c392d34",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/14.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "===================================================================================================================\n",
              "Layer (type (var_name))                                           Input Shape               Output Shape\n",
              "===================================================================================================================\n",
              "EfficientX3d                                                      --                        --\n",
              "├─Sequential (s1)                                                 [1, 3, 8, 224, 224]       [1, 24, 8, 112, 112]\n",
              "│    └─Conv3dTemporalKernel1BnAct (pathway0_stem_conv_xy)         [1, 3, 8, 224, 224]       [1, 24, 8, 112, 112]\n",
              "│    │    └─Sequential (kernel)                                   [1, 3, 8, 224, 224]       [1, 24, 8, 112, 112]\n",
              "│    │    │    └─Conv3d (conv)                                    [1, 3, 8, 224, 224]       [1, 24, 8, 112, 112]\n",
              "│    │    │    └─Identity (act)                                   [1, 24, 8, 112, 112]      [1, 24, 8, 112, 112]\n",
              "│    └─Conv3d5x1x1BnAct (pathway0_stem_conv)                      [1, 24, 8, 112, 112]      [1, 24, 8, 112, 112]\n",
              "│    │    └─Sequential (kernel)                                   [1, 24, 8, 112, 112]      [1, 24, 8, 112, 112]\n",
              "│    │    │    └─Conv3d (conv)                                    [1, 24, 8, 112, 112]      [1, 24, 8, 112, 112]\n",
              "│    │    │    └─BatchNorm3d (bn)                                 [1, 24, 8, 112, 112]      [1, 24, 8, 112, 112]\n",
              "│    │    │    └─ReLU (act)                                       [1, 24, 8, 112, 112]      [1, 24, 8, 112, 112]\n",
              "├─Sequential (s2)                                                 [1, 24, 8, 112, 112]      [1, 24, 8, 56, 56]\n",
              "│    └─X3dBottleneckBlock (pathway0_res0)                         [1, 24, 8, 112, 112]      [1, 24, 8, 56, 56]\n",
              "│    │    └─Sequential (layers)                                   [1, 24, 8, 112, 112]      [1, 24, 8, 56, 56]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 24, 8, 112, 112]      [1, 54, 8, 112, 112]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 54, 8, 112, 112]      [1, 54, 8, 56, 56]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 54, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 54, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 54, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─Conv3dTemporalKernel1BnAct (_res_proj)                [1, 24, 8, 112, 112]      [1, 24, 8, 56, 56]\n",
              "│    │    │    └─Sequential (kernel)                              [1, 24, 8, 112, 112]      [1, 24, 8, 56, 56]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─ReLU (final_act)                                      [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    │    └─ReLU (act)                                       [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    └─X3dBottleneckBlock (pathway0_res1)                         [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─Sequential (layers)                                   [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 24, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 54, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 54, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 54, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─ReLU (final_act)                                      [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    │    └─ReLU (act)                                       [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    └─X3dBottleneckBlock (pathway0_res2)                         [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─Sequential (layers)                                   [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 24, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 54, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 54, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 54, 8, 56, 56]        [1, 54, 8, 56, 56]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 54, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    └─ReLU (final_act)                                      [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "│    │    │    └─ReLU (act)                                       [1, 24, 8, 56, 56]        [1, 24, 8, 56, 56]\n",
              "├─Sequential (s3)                                                 [1, 24, 8, 56, 56]        [1, 48, 8, 28, 28]\n",
              "│    └─X3dBottleneckBlock (pathway0_res0)                         [1, 24, 8, 56, 56]        [1, 48, 8, 28, 28]\n",
              "│    │    └─Sequential (layers)                                   [1, 24, 8, 56, 56]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 24, 8, 56, 56]        [1, 108, 8, 56, 56]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 108, 8, 56, 56]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 108, 8, 28, 28]       [1, 48, 8, 28, 28]\n",
              "│    │    └─Conv3dTemporalKernel1BnAct (_res_proj)                [1, 24, 8, 56, 56]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─Sequential (kernel)                              [1, 24, 8, 56, 56]        [1, 48, 8, 28, 28]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─ReLU (final_act)                                      [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─ReLU (act)                                       [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    └─X3dBottleneckBlock (pathway0_res1)                         [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─Sequential (layers)                                   [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 48, 8, 28, 28]        [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 108, 8, 28, 28]       [1, 48, 8, 28, 28]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─ReLU (final_act)                                      [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─ReLU (act)                                       [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    └─X3dBottleneckBlock (pathway0_res2)                         [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─Sequential (layers)                                   [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 48, 8, 28, 28]        [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 108, 8, 28, 28]       [1, 48, 8, 28, 28]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─ReLU (final_act)                                      [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─ReLU (act)                                       [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    └─X3dBottleneckBlock (pathway0_res3)                         [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─Sequential (layers)                                   [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 48, 8, 28, 28]        [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 108, 8, 28, 28]       [1, 48, 8, 28, 28]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─ReLU (final_act)                                      [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─ReLU (act)                                       [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    └─X3dBottleneckBlock (pathway0_res4)                         [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─Sequential (layers)                                   [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 48, 8, 28, 28]        [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 108, 8, 28, 28]       [1, 108, 8, 28, 28]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 108, 8, 28, 28]       [1, 48, 8, 28, 28]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    └─ReLU (final_act)                                      [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "│    │    │    └─ReLU (act)                                       [1, 48, 8, 28, 28]        [1, 48, 8, 28, 28]\n",
              "├─Sequential (s4)                                                 [1, 48, 8, 28, 28]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res0)                         [1, 48, 8, 28, 28]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 48, 8, 28, 28]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 48, 8, 28, 28]        [1, 216, 8, 28, 28]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 28, 28]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─Conv3dTemporalKernel1BnAct (_res_proj)                [1, 48, 8, 28, 28]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Sequential (kernel)                              [1, 48, 8, 28, 28]        [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res1)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res2)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res3)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res4)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res5)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res6)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res7)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res8)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res9)                         [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    └─X3dBottleneckBlock (pathway0_res10)                        [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 216, 8, 14, 14]       [1, 216, 8, 14, 14]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 216, 8, 14, 14]       [1, 96, 8, 14, 14]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    └─ReLU (final_act)                                      [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "│    │    │    └─ReLU (act)                                       [1, 96, 8, 14, 14]        [1, 96, 8, 14, 14]\n",
              "├─Sequential (s5)                                                 [1, 96, 8, 14, 14]        [1, 192, 8, 7, 7]\n",
              "│    └─X3dBottleneckBlock (pathway0_res0)                         [1, 96, 8, 14, 14]        [1, 192, 8, 7, 7]\n",
              "│    │    └─Sequential (layers)                                   [1, 96, 8, 14, 14]        [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 96, 8, 14, 14]        [1, 432, 8, 14, 14]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 432, 8, 14, 14]       [1, 432, 8, 7, 7]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 432, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─Conv3dTemporalKernel1BnAct (_res_proj)                [1, 96, 8, 14, 14]        [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Sequential (kernel)                              [1, 96, 8, 14, 14]        [1, 192, 8, 7, 7]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─ReLU (final_act)                                      [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    └─X3dBottleneckBlock (pathway0_res1)                         [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─Sequential (layers)                                   [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 432, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─ReLU (final_act)                                      [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    └─X3dBottleneckBlock (pathway0_res2)                         [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─Sequential (layers)                                   [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 432, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─ReLU (final_act)                                      [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    └─X3dBottleneckBlock (pathway0_res3)                         [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─Sequential (layers)                                   [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 432, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─ReLU (final_act)                                      [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    └─X3dBottleneckBlock (pathway0_res4)                         [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─Sequential (layers)                                   [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 432, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─ReLU (final_act)                                      [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    └─X3dBottleneckBlock (pathway0_res5)                         [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─Sequential (layers)                                   [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 432, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─ReLU (final_act)                                      [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    └─X3dBottleneckBlock (pathway0_res6)                         [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─Sequential (layers)                                   [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_0)                           [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3d3x3x3DwBnAct (conv_1)                      [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─SqueezeExcitation (se)                           [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Swish (act_func_1)                               [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3dPwBnAct (conv_2)                           [1, 432, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─FloatFunctional (_residual_add_func)                  --                        --\n",
              "│    │    │    └─Identity (activation_post_process)               [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    └─ReLU (final_act)                                      [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 192, 8, 7, 7]         [1, 192, 8, 7, 7]\n",
              "├─Sequential (head)                                               [1, 192, 8, 7, 7]         [1, 2048, 1, 1, 1]\n",
              "│    └─Conv3dPwBnAct (conv_5)                                     [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    └─Sequential (kernel)                                   [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─Conv3d (conv)                                    [1, 192, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─BatchNorm3d (bn)                                 [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    │    │    └─ReLU (act)                                       [1, 432, 8, 7, 7]         [1, 432, 8, 7, 7]\n",
              "│    └─AdaptiveAvgPool3dOutSize1 (avg_pool)                       [1, 432, 8, 7, 7]         [1, 432, 1, 1, 1]\n",
              "│    │    └─AdaptiveAvgPool3d (pool)                              [1, 432, 8, 7, 7]         [1, 432, 1, 1, 1]\n",
              "│    └─Conv3dPwBnAct (lin_5)                                      [1, 432, 1, 1, 1]         [1, 2048, 1, 1, 1]\n",
              "│    │    └─Sequential (kernel)                                   [1, 432, 1, 1, 1]         [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─Conv3d (conv)                                    [1, 432, 1, 1, 1]         [1, 2048, 1, 1, 1]\n",
              "│    │    │    └─ReLU (act)                                       [1, 2048, 1, 1, 1]        [1, 2048, 1, 1, 1]\n",
              "├─Dropout (dropout)                                               [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 2048]\n",
              "├─FullyConnected (projection)                                     [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "│    └─Linear (model)                                             [1, 1, 1, 1, 2048]        [1, 1, 1, 1, 400]\n",
              "├─Identity (act)                                                  [1, 1, 1, 1, 400]         [1, 1, 1, 1, 400]\n",
              "│    └─Identity (act)                                             [1, 1, 1, 1, 400]         [1, 1, 1, 1, 400]\n",
              "===================================================================================================================\n",
              "Total params: 3,794,322\n",
              "Trainable params: 3,794,322\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 2.37\n",
              "===================================================================================================================\n",
              "Input size (MB): 4.82\n",
              "Forward/backward pass size (MB): 684.05\n",
              "Params size (MB): 15.18\n",
              "Estimated Total Size (MB): 704.04\n",
              "==================================================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p8_I5u2QHbs"
      },
      "source": [
        ""
      ],
      "execution_count": 16,
      "outputs": []
    }
  ]
}