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            "object_fit": null,
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            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
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        },
        "c9f5373bd31e4fb38aa724fba87b8a4e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
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        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pkirti33/video_classification_pytorch/blob/main/video_classification_minir3d18.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -e ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufWbQwFQbQKQ",
        "outputId": "201d1c81-e34c-4886-c2a7-8d8250604ae7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.3/34.3 MB\u001b[0m \u001b[31m19.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.7/132.7 kB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m868.8/868.8 kB\u001b[0m \u001b[31m53.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m47.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m78.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pytorchvideo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "RKmKfSs_vXVt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# device agnostic code\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YKYnrFQhAc0",
        "outputId": "885582ac-ec07-4457-dd70-82363ca90ac1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Working with Data"
      ],
      "metadata": {
        "id": "HnGV8j0Zgg8n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Upload Data from Kaggle"
      ],
      "metadata": {
        "id": "76zpcYC8gsr9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import ipdb\n",
        "import av\n",
        "from torch import nn\n",
        "import os\n",
        "import shutil\n",
        "import random\n",
        "import zipfile"
      ],
      "metadata": {
        "id": "Gn0qTZzrgvAq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# PREP KAGGLE API AND DOWNLOAD DATA\n",
        "\n",
        "# upload kaggle.json here\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "\n",
        "# make kaggle directory and upload .json file there\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "# download kaggle data from https://www.kaggle.com/datasets/khanhvudo72/ucf11updated\n",
        "! kaggle datasets download -d khanhvudo72/ucf11updated"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "2OPtdoqggwmw",
        "outputId": "26ddac9f-6efd-401b-bdca-ff73e6d1fc1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d207e131-6780-495a-bc0d-6eb5061eb3c8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d207e131-6780-495a-bc0d-6eb5061eb3c8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "Dataset URL: https://www.kaggle.com/datasets/khanhvudo72/ucf11updated\n",
            "License(s): unknown\n",
            "Downloading ucf11updated.zip to /content\n",
            "100% 0.98G/0.98G [01:02<00:00, 21.4MB/s]\n",
            "100% 0.98G/0.98G [01:02<00:00, 17.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ORGANIZE DATA INTO FOLDERS AND UNZIP\n",
        "\n",
        "# create new folder \"data\" at base level of the Google Colab directory\n",
        "data_folder = '/content/data'\n",
        "if not os.path.exists(data_folder):\n",
        "    os.makedirs(data_folder)\n",
        "\n",
        "# extract the contents of the zip file into the \"data\" folder\n",
        "zip_file_path = '/content/ucf11updated.zip'\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(data_folder)\n",
        "\n",
        "print(\"Extraction complete.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFjSwiWJg2gi",
        "outputId": "80bb8172-8c1a-4120-b55b-d4089af613f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extraction complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE UNECESSARY FOLDERS\n",
        "\n",
        "data_folder = '/content/data/UCF11_updated_mpg'\n",
        "\n",
        "# iterate through all subdirectories within the data folder\n",
        "for root, dirs, files in os.walk(data_folder):\n",
        "    for dir_name in dirs:\n",
        "        if dir_name == 'Annotation':\n",
        "            # delete the 'Annotation' folder\n",
        "            folder_path = os.path.join(root, dir_name)\n",
        "            shutil.rmtree(folder_path)\n",
        "            print(f\"Removed '{dir_name}' folder from '{root}'\")\n",
        "\n",
        "print(\"Removal complete.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUGDMtrWg_Os",
        "outputId": "b0bd9f65-498e-4af0-ed6c-23df250bf25b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/horse_riding'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/basketball'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/walking'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/volleyball_spiking'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/biking'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/tennis_swing'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/diving'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/soccer_juggling'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/trampoline_jumping'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/golf_swing'\n",
            "Removed 'Annotation' folder from '/content/data/UCF11_updated_mpg/swing'\n",
            "Removal complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# EMPTY ALL SUBFOLDERS OF THE VIDEO CLASS FOLDERS (CLEANUP)\n",
        "\n",
        "data_folder = '/content/data/UCF11_updated_mpg'\n",
        "\n",
        "# iterate through all video class folders\n",
        "for video_class in os.listdir(data_folder):\n",
        "    class_folder = os.path.join(data_folder, video_class)\n",
        "\n",
        "    # skip non-directory files\n",
        "    if not os.path.isdir(class_folder):\n",
        "        continue\n",
        "\n",
        "    # iterate through subdirectories within the video class folder\n",
        "    for root, dirs, files in os.walk(class_folder):\n",
        "        for dir_name in dirs:\n",
        "            dir_path = os.path.join(root, dir_name)\n",
        "\n",
        "            # move files from the subfolder to the video class folder\n",
        "            for file_name in os.listdir(dir_path):\n",
        "                file_path = os.path.join(dir_path, file_name)\n",
        "                shutil.move(file_path, class_folder)\n",
        "\n",
        "            # remove the subfolder\n",
        "            shutil.rmtree(dir_path)\n",
        "\n",
        "print(\"Cleanup complete.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Zmdfio-hHr0",
        "outputId": "3ea038dd-e14e-4eb1-fe5d-0f15b960c0b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleanup complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ORGANIZE TRAIN AND TEST FOLDERS\n",
        "\n",
        "data_folder = '/content/data/UCF11_updated_mpg'\n",
        "train_folder = os.path.join(data_folder, 'train')\n",
        "test_folder = os.path.join(data_folder, 'test')\n",
        "\n",
        "# create 'train' and 'test' folders\n",
        "video_class_list = os.listdir(data_folder)\n",
        "os.mkdir(train_folder)\n",
        "os.mkdir(test_folder)\n",
        "\n",
        "# iterate through all video-class folders\n",
        "for video_class in video_class_list:\n",
        "    class_folder = os.path.join(data_folder, video_class)\n",
        "\n",
        "    # skip non-directory files and 'train'/'test' folders\n",
        "    if not os.path.isdir(class_folder) or video_class in ['train', 'test']:\n",
        "        continue\n",
        "\n",
        "    # create train-sub-video-class folder\n",
        "    train_class_folder = os.path.join(train_folder, video_class)\n",
        "    os.mkdir(train_class_folder)\n",
        "\n",
        "    # create test-sub-video-class folder\n",
        "    test_class_folder = os.path.join(test_folder, video_class)\n",
        "    os.mkdir(test_class_folder)\n",
        "\n",
        "    # get list of videos in the video-class folder\n",
        "    video_files = os.listdir(class_folder)\n",
        "    num_videos = len(video_files)\n",
        "\n",
        "    # calculate the split indices for train and test\n",
        "    train_split = int(0.8 * num_videos)\n",
        "    test_split = num_videos - train_split\n",
        "\n",
        "    # move videos to train-sub-video-class folder\n",
        "    for file_name in video_files[:train_split]:\n",
        "        src_file_path = os.path.join(class_folder, file_name)\n",
        "        dst_file_path = os.path.join(train_class_folder, file_name)\n",
        "        shutil.move(src_file_path, dst_file_path)\n",
        "\n",
        "    # move videos to test-sub-video-class folder\n",
        "    for file_name in video_files[train_split:]:\n",
        "        src_file_path = os.path.join(class_folder, file_name)\n",
        "        dst_file_path = os.path.join(test_class_folder, file_name)\n",
        "        shutil.move(src_file_path, dst_file_path)\n",
        "\n",
        "    # remove the video-class folder if empty\n",
        "    if not os.listdir(class_folder):\n",
        "        os.rmdir(class_folder)\n",
        "        print(f\"Removed empty '{video_class}' folder\")\n",
        "\n",
        "print(\"Data split and cleanup complete.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnmopOt1hMSo",
        "outputId": "a9da039d-18a9-4242-995a-e8b32a56ffa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Removed empty 'horse_riding' folder\n",
            "Removed empty 'basketball' folder\n",
            "Removed empty 'walking' folder\n",
            "Removed empty 'volleyball_spiking' folder\n",
            "Removed empty 'biking' folder\n",
            "Removed empty 'tennis_swing' folder\n",
            "Removed empty 'diving' folder\n",
            "Removed empty 'soccer_juggling' folder\n",
            "Removed empty 'trampoline_jumping' folder\n",
            "Removed empty 'golf_swing' folder\n",
            "Removed empty 'swing' folder\n",
            "Data split and cleanup complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Making the Custom Dataset"
      ],
      "metadata": {
        "id": "yj8Wok9whOP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pytorchvideo\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from pathlib import Path\n",
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "metadata": {
        "id": "vHezPmuphRsL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MAKE CLASSES AND CLASS_TO_IDX\n",
        "def find_classes(dir: str) -> tuple[list[str], dict[str, int]]:\n",
        "  classes = sorted(os.listdir(dir))\n",
        "\n",
        "  class_to_idx={}\n",
        "  for i, video_class in enumerate(classes):\n",
        "    class_to_idx[video_class] = i\n",
        "\n",
        "  return classes, class_to_idx"
      ],
      "metadata": {
        "id": "Q1ZLgUdOhTKg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MAKE THE CUSTOM DATASET\n",
        "\n",
        "class VideoFolder(Dataset):\n",
        "\n",
        "  def __init__(self, targ_dir: str, transform=None):\n",
        "    self.paths = sorted(list(Path(targ_dir).glob(\"*/*.mpg\")))\n",
        "    self.transform = transform\n",
        "    self.classes, self.class_to_idx = find_classes(targ_dir)\n",
        "\n",
        "  def load_video(self, idx: int):\n",
        "    video_path = self.paths[idx]\n",
        "    return torchvision.io.read_video(filename=str(video_path),\n",
        "                                     start_pts=0,\n",
        "                                     end_pts=5,\n",
        "                                     pts_unit='sec') #outputs with THWC\n",
        "\n",
        "  def __len__(self) -> int:\n",
        "    return len(self.paths)\n",
        "\n",
        "  def __getitem__(self, idx: int) -> tuple[torch.Tensor, int]:\n",
        "    loaded_video = self.load_video(idx)\n",
        "\n",
        "    video_class = self.paths[idx].parent.name\n",
        "    video_key = self.class_to_idx[video_class]\n",
        "\n",
        "    if self.transform:\n",
        "      return self.transform(loaded_video[0].type(torch.float32)).type(torch.uint8), video_key\n",
        "    else:\n",
        "      return loaded_video[0].type(torch.uint8), video_key"
      ],
      "metadata": {
        "id": "mvn0BL5NhZKV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Video Transformations"
      ],
      "metadata": {
        "id": "z_mp4d_Zha3a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Custom Transformation Classes"
      ],
      "metadata": {
        "id": "YW2nTKi2hiXp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ProportionalFrameCrop(object):\n",
        "  \"\"\"\n",
        "  Given a proportion X, remove X*(total number of frames) from the beginning\n",
        "  and end of video tensor.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, proportion):\n",
        "    self.proportion = proportion\n",
        "\n",
        "  def __call__(self, video):\n",
        "    # Calculate the number of frames to remove from the beginning and end\n",
        "    num_frames = video.shape[0]\n",
        "    num_frames_to_remove = int(self.proportion * num_frames)\n",
        "\n",
        "    # Trim the video tensor\n",
        "    if num_frames_to_remove > 0:\n",
        "        video = video[num_frames_to_remove:-num_frames_to_remove]\n",
        "\n",
        "    # Transpose the tensor to the desired shape\n",
        "    video = video.permute(3, 0, 1, 2)\n",
        "\n",
        "    return video"
      ],
      "metadata": {
        "id": "nGe7XRuEhksE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transformation Sequence"
      ],
      "metadata": {
        "id": "OJX1xwd7iXLm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TRANSFORMATION FOR VIDEOS CROPPED PROPRTIONALLY BY FRAMES\n",
        "transform_1proportion = transforms.Compose([\n",
        "    ProportionalFrameCrop(proportion=0.1) #wants THWC, returns CTHW\n",
        "])"
      ],
      "metadata": {
        "id": "LmkKyhJ4iXE2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Testing Transformations"
      ],
      "metadata": {
        "id": "QsF3GXo6ivve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TESTING THE TRANSFORMATIONS\n",
        "def test_transformation(video_path: str,\n",
        "                        transformation: transforms.Compose,\n",
        "                        output_path: str):\n",
        "\n",
        "  loaded_video = torchvision.io.read_video(filename=video_path,\n",
        "                                           start_pts=0, #experiment to find out what these do\n",
        "                                           end_pts=5,\n",
        "                                           pts_unit='sec') #outputs with THWC\n",
        "\n",
        "  transformed_video = transformation(loaded_video[0].type(torch.float32))\n",
        "  transformed_video = transformed_video.permute((1, 2, 3, 0)).type(torch.uint8)\n",
        "\n",
        "  print(f\"Loaded video shape: {loaded_video[0].shape}\")\n",
        "  print(f\"Transformed video shape: {transformed_video.shape}\")\n",
        "\n",
        "  torchvision.io.write_video(filename=output_path,\n",
        "                             video_array=transformed_video, #in THWC\n",
        "                             fps = loaded_video[2]['video_fps'],\n",
        "                             video_codec='mpeg1video')"
      ],
      "metadata": {
        "id": "k656GaPXi2P7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.mkdir('/content/test_transformations')\n",
        "files = os.listdir(\"/content/data/UCF11_updated_mpg/test/basketball\")\n",
        "file_name = files[0]\n",
        "video_path = os.path.join(\"/content/data/UCF11_updated_mpg/test/basketball\", file_name)\n",
        "video_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5SXfSP1li7AE",
        "outputId": "0bf29684-855b-45fc-978b-ec7efa5dd7bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/data/UCF11_updated_mpg/test/basketball/v_shooting_19_08.mpg'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#TESTING TRIMMING OFF 0.1 PROPORTION\n",
        "test_transformation(video_path=video_path,\n",
        "                    transformation=transform_1proportion,\n",
        "                    output_path='/content/test_transformations/1proportion_testing.mpg')"
      ],
      "metadata": {
        "id": "zrwb5n8p7rF1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "706c7317-b340-456e-d02c-fe6eae69646d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded video shape: torch.Size([120, 240, 320, 3])\n",
            "Transformed video shape: torch.Size([96, 240, 320, 3])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving Transformed Video Directories"
      ],
      "metadata": {
        "id": "dm66OHV3jFdn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def contains_dirs(directory: str) -> bool:\n",
        "  \"\"\"\n",
        "  Helper Function:\n",
        "  Given a directory, checks if that directory contains only other directories.\n",
        "  If so, returns True. If it contains at least one file, returns False.\n",
        "  \"\"\"\n",
        "\n",
        "  checker = True #True indicates only directories\n",
        "  for dir in os.listdir(directory):\n",
        "    path_to_check = os.path.join(directory, dir)\n",
        "    if os.path.isfile(path_to_check):\n",
        "      checker = False #False indicates a file was detected\n",
        "      break\n",
        "\n",
        "  return checker"
      ],
      "metadata": {
        "id": "IikrsPuHjR8C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_transformed_videos_recursive(in_directory: str,\n",
        "                                      out_directory: str,\n",
        "                                      transformation: transforms.Compose):\n",
        "  \"\"\"\n",
        "  Takes all videos inside in_directory, performs the given transformation, and\n",
        "  places them inside out_directory. If in_directory contains subdirectories,\n",
        "  this structure is copied into out_directory.\n",
        "  \"\"\"\n",
        "  os.mkdir(out_directory)\n",
        "  if not contains_dirs(in_directory):\n",
        "    for video in os.listdir(in_directory):\n",
        "      if not video.startswith('.'):\n",
        "        next_video_in_dir = os.path.join(in_directory, video)\n",
        "        next_video_out_dir = os.path.join(out_directory, video)\n",
        "        #print(f\"video to be transformed: {next_video_in_dir}\")\n",
        "        test_transformation(next_video_in_dir, transformation, next_video_out_dir)\n",
        "    print(f\"Successfully transformed videos from {in_directory} and put into {out_directory}\\n\")\n",
        "\n",
        "  else:\n",
        "    for dir in os.listdir(in_directory):\n",
        "      next_in_dir = os.path.join(in_directory, dir)\n",
        "      next_out_dir = os.path.join(out_directory, dir)\n",
        "      #print(f\"next in_dir: {next_in_dir} | next out_dir: {next_out_dir}\")\n",
        "      save_transformed_videos_recursive(next_in_dir, next_out_dir, transformation)"
      ],
      "metadata": {
        "id": "r8sgrTNCjmTJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Making DataLoaders"
      ],
      "metadata": {
        "id": "IU5Chb9wkbnK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Assign directories\n",
        "test_dir = '/content/data/UCF11_updated_mpg/test'\n",
        "train_dir = '/content/data/UCF11_updated_mpg/train'\n",
        "\n",
        "#Make the datasets\n",
        "train_dataset_1proportion= VideoFolder(train_dir, transform_1proportion)\n",
        "test_dataset_1proportion= VideoFolder(test_dir, transform_1proportion)"
      ],
      "metadata": {
        "id": "u8qwDgP5keh3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Make the dataloaders\n",
        "BATCH_SIZE = 1\n",
        "NUM_WORKERS = 0\n",
        "\n",
        "train_dataloader_1proportion = DataLoader(\n",
        "    dataset=train_dataset_1proportion,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    num_workers=NUM_WORKERS,\n",
        "    shuffle=True)\n",
        "test_dataloader_1proportion = DataLoader(\n",
        "    dataset=test_dataset_1proportion,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    num_workers=NUM_WORKERS,\n",
        "    shuffle=False)"
      ],
      "metadata": {
        "id": "hUGG7HnFkvYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_given_dataloader(dataloader: DataLoader):\n",
        "  tensor, label = next(iter(dataloader))\n",
        "\n",
        "  print(f\"Video shape: {tensor.shape} | Label: {label} | Data Type: {tensor.dtype}\")"
      ],
      "metadata": {
        "id": "Zu5gm0FMk2Bg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TEST A DATALOADER HERE\n",
        "\n",
        "test_given_dataloader(train_dataloader_1proportion)\n",
        "test_given_dataloader(test_dataloader_1proportion)"
      ],
      "metadata": {
        "id": "x_RztzbflVD_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c44bd067-6423-4ebf-cc96-2f36d0b19c6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Video shape: torch.Size([1, 3, 68, 240, 320]) | Label: tensor([3]) | Data Type: torch.uint8\n",
            "Video shape: torch.Size([1, 3, 108, 240, 320]) | Label: tensor([0]) | Data Type: torch.uint8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Make the Model"
      ],
      "metadata": {
        "id": "9KSPm7DPlbu0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torchinfo"
      ],
      "metadata": {
        "id": "6LAQMfVLldxR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examining a Pre-Trained Model\n",
        "\n",
        "My model's architecture is going to based off of this one. I chose ResNet R3D_18 from torchvision since it is easily accessible, built for action recognition, and trained on UCF101.\n",
        "\n",
        "Currently commented out since my model has been built."
      ],
      "metadata": {
        "id": "WmaLIrQUle4H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()\n",
        "\n",
        "model_premade = torchvision.models.video.r3d_18(weights='DEFAULT', progress=True).to(device)\n",
        "torchinfo.summary(model_premade, input_size=[1, 3, 50, 200, 300])"
      ],
      "metadata": {
        "id": "9IlNYganljHu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "902292c9-95b4-49f8-a5c9-b38b0390a0a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/r3d_18-b3b3357e.pth\" to /root/.cache/torch/hub/checkpoints/r3d_18-b3b3357e.pth\n",
            "100%|██████████| 127M/127M [00:00<00:00, 174MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==========================================================================================\n",
              "Layer (type:depth-idx)                   Output Shape              Param #\n",
              "==========================================================================================\n",
              "VideoResNet                              [1, 400]                  --\n",
              "├─BasicStem: 1-1                         [1, 64, 50, 100, 150]     --\n",
              "│    └─Conv3d: 2-1                       [1, 64, 50, 100, 150]     28,224\n",
              "│    └─BatchNorm3d: 2-2                  [1, 64, 50, 100, 150]     128\n",
              "│    └─ReLU: 2-3                         [1, 64, 50, 100, 150]     --\n",
              "├─Sequential: 1-2                        [1, 64, 50, 100, 150]     --\n",
              "│    └─BasicBlock: 2-4                   [1, 64, 50, 100, 150]     --\n",
              "│    │    └─Sequential: 3-1              [1, 64, 50, 100, 150]     110,720\n",
              "│    │    └─Sequential: 3-2              [1, 64, 50, 100, 150]     110,720\n",
              "│    │    └─ReLU: 3-3                    [1, 64, 50, 100, 150]     --\n",
              "│    └─BasicBlock: 2-5                   [1, 64, 50, 100, 150]     --\n",
              "│    │    └─Sequential: 3-4              [1, 64, 50, 100, 150]     110,720\n",
              "│    │    └─Sequential: 3-5              [1, 64, 50, 100, 150]     110,720\n",
              "│    │    └─ReLU: 3-6                    [1, 64, 50, 100, 150]     --\n",
              "├─Sequential: 1-3                        [1, 128, 25, 50, 75]      --\n",
              "│    └─BasicBlock: 2-6                   [1, 128, 25, 50, 75]      --\n",
              "│    │    └─Sequential: 3-7              [1, 128, 25, 50, 75]      221,440\n",
              "│    │    └─Sequential: 3-8              [1, 128, 25, 50, 75]      442,624\n",
              "│    │    └─Sequential: 3-9              [1, 128, 25, 50, 75]      8,448\n",
              "│    │    └─ReLU: 3-10                   [1, 128, 25, 50, 75]      --\n",
              "│    └─BasicBlock: 2-7                   [1, 128, 25, 50, 75]      --\n",
              "│    │    └─Sequential: 3-11             [1, 128, 25, 50, 75]      442,624\n",
              "│    │    └─Sequential: 3-12             [1, 128, 25, 50, 75]      442,624\n",
              "│    │    └─ReLU: 3-13                   [1, 128, 25, 50, 75]      --\n",
              "├─Sequential: 1-4                        [1, 256, 13, 25, 38]      --\n",
              "│    └─BasicBlock: 2-8                   [1, 256, 13, 25, 38]      --\n",
              "│    │    └─Sequential: 3-14             [1, 256, 13, 25, 38]      885,248\n",
              "│    │    └─Sequential: 3-15             [1, 256, 13, 25, 38]      1,769,984\n",
              "│    │    └─Sequential: 3-16             [1, 256, 13, 25, 38]      33,280\n",
              "│    │    └─ReLU: 3-17                   [1, 256, 13, 25, 38]      --\n",
              "│    └─BasicBlock: 2-9                   [1, 256, 13, 25, 38]      --\n",
              "│    │    └─Sequential: 3-18             [1, 256, 13, 25, 38]      1,769,984\n",
              "│    │    └─Sequential: 3-19             [1, 256, 13, 25, 38]      1,769,984\n",
              "│    │    └─ReLU: 3-20                   [1, 256, 13, 25, 38]      --\n",
              "├─Sequential: 1-5                        [1, 512, 7, 13, 19]       --\n",
              "│    └─BasicBlock: 2-10                  [1, 512, 7, 13, 19]       --\n",
              "│    │    └─Sequential: 3-21             [1, 512, 7, 13, 19]       3,539,968\n",
              "│    │    └─Sequential: 3-22             [1, 512, 7, 13, 19]       7,078,912\n",
              "│    │    └─Sequential: 3-23             [1, 512, 7, 13, 19]       132,096\n",
              "│    │    └─ReLU: 3-24                   [1, 512, 7, 13, 19]       --\n",
              "│    └─BasicBlock: 2-11                  [1, 512, 7, 13, 19]       --\n",
              "│    │    └─Sequential: 3-25             [1, 512, 7, 13, 19]       7,078,912\n",
              "│    │    └─Sequential: 3-26             [1, 512, 7, 13, 19]       7,078,912\n",
              "│    │    └─ReLU: 3-27                   [1, 512, 7, 13, 19]       --\n",
              "├─AdaptiveAvgPool3d: 1-6                 [1, 512, 1, 1, 1]         --\n",
              "├─Linear: 1-7                            [1, 400]                  205,200\n",
              "==========================================================================================\n",
              "Total params: 33,371,472\n",
              "Trainable params: 33,371,472\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 618.81\n",
              "==========================================================================================\n",
              "Input size (MB): 36.00\n",
              "Forward/backward pass size (MB): 5123.75\n",
              "Params size (MB): 133.49\n",
              "Estimated Total Size (MB): 5293.24\n",
              "=========================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Writing My Own Model"
      ],
      "metadata": {
        "id": "oZOpV4c_l5XJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REAL MODEL I WILL USE, BASED ON RESNET R3D18\n",
        "class MiniR3D18(nn.Module):\n",
        "  def __init__(self):\n",
        "      super().__init__()\n",
        "\n",
        "      #the stem\n",
        "      self.stem = nn.Sequential(nn.Conv3d(in_channels=3,\n",
        "                                          out_channels=20,\n",
        "                                          kernel_size=4,\n",
        "                                          stride=2,\n",
        "                                          padding=2,\n",
        "                                          bias=False), #wants [batchsize, color, frames, height, width]\n",
        "                                nn.BatchNorm3d(num_features=20, track_running_stats=False),\n",
        "                                nn.ReLU(inplace=True))\n",
        "\n",
        "      #the first layer - convolution\n",
        "      conv_1_first = nn.Sequential(nn.Conv3d(in_channels=20, out_channels=20, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                   nn.BatchNorm3d(num_features=20, track_running_stats=False),\n",
        "                                   nn.ReLU(inplace=True))\n",
        "      conv_2_first = nn.Sequential(nn.Conv3d(in_channels=20, out_channels=20, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                   nn.BatchNorm3d(num_features=20, track_running_stats=False))\n",
        "      conv_3_first = nn.Sequential(nn.Conv3d(in_channels=20, out_channels=20, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                   nn.BatchNorm3d(num_features=20, track_running_stats=False),\n",
        "                                   nn.ReLU(inplace=True))\n",
        "      conv_4_first = nn.Sequential(nn.Conv3d(in_channels=20, out_channels=20, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                   nn.BatchNorm3d(num_features=20, track_running_stats=False))\n",
        "      self.layer_1 = nn.Sequential(conv_1_first, conv_2_first, nn.ReLU(), conv_3_first, conv_4_first, nn.ReLU())\n",
        "\n",
        "      #the second layer - convolution REMOVED DOWNSAMPLE\n",
        "      conv_1_second = nn.Sequential(nn.Conv3d(in_channels=20, out_channels=40, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                    nn.BatchNorm3d(num_features=40, track_running_stats=False),\n",
        "                                    nn.ReLU(inplace=True))\n",
        "      conv_2_second = nn.Sequential(nn.Conv3d(in_channels=40, out_channels=40, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                    nn.BatchNorm3d(num_features=40, track_running_stats=False))\n",
        "      #downsample = nn.Sequential(nn.Conv3d(in_channels=20, out_channels=40, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                 #nn.BatchNorm3d(num_features=40, track_running_stats=False))\n",
        "      conv_3_second = nn.Sequential(nn.Conv3d(in_channels=40, out_channels=40, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                    nn.BatchNorm3d(num_features=40, track_running_stats=False),\n",
        "                                    nn.ReLU(inplace=True))\n",
        "      conv_4_second = nn.Sequential(nn.Conv3d(in_channels=40, out_channels=40, kernel_size=4, stride=2, padding=2, bias=False),\n",
        "                                    nn.BatchNorm3d(num_features=40, track_running_stats=False))\n",
        "      self.layer_2 = nn.Sequential(conv_1_second, conv_2_second, nn.ReLU(), conv_3_second, conv_4_second, nn.ReLU())\n",
        "\n",
        "      #the third layer - avgpool\n",
        "      self.avgpool = nn.AdaptiveAvgPool3d(output_size=1)\n",
        "\n",
        "      #the fourth layer - classifier\n",
        "      self.classifier = nn.Linear(in_features=40, out_features=11, bias=True)\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.classifier(self.avgpool(self.layer_2(self.layer_1(self.stem(x)))).squeeze(-1).squeeze(-1).squeeze(-1))"
      ],
      "metadata": {
        "id": "yQUxNvuel7Wx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#take a look at the model\n",
        "model_mini_r3d18 = MiniR3D18().to(device)\n",
        "torchinfo.summary(model_mini_r3d18, input_size=[1, 3, 50, 200, 300])"
      ],
      "metadata": {
        "id": "jr0orlnhmBOR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2950b7a-2040-4199-d590-57e9eb87b50d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:605: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return F.conv3d(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==========================================================================================\n",
              "Layer (type:depth-idx)                   Output Shape              Param #\n",
              "==========================================================================================\n",
              "MiniR3D18                                [1, 11]                   --\n",
              "├─Sequential: 1-1                        [1, 20, 26, 101, 151]     --\n",
              "│    └─Conv3d: 2-1                       [1, 20, 26, 101, 151]     3,840\n",
              "│    └─BatchNorm3d: 2-2                  [1, 20, 26, 101, 151]     40\n",
              "│    └─ReLU: 2-3                         [1, 20, 26, 101, 151]     --\n",
              "├─Sequential: 1-2                        [1, 20, 3, 8, 11]         --\n",
              "│    └─Sequential: 2-4                   [1, 20, 14, 51, 76]       --\n",
              "│    │    └─Conv3d: 3-1                  [1, 20, 14, 51, 76]       25,600\n",
              "│    │    └─BatchNorm3d: 3-2             [1, 20, 14, 51, 76]       40\n",
              "│    │    └─ReLU: 3-3                    [1, 20, 14, 51, 76]       --\n",
              "│    └─Sequential: 2-5                   [1, 20, 8, 26, 39]        --\n",
              "│    │    └─Conv3d: 3-4                  [1, 20, 8, 26, 39]        25,600\n",
              "│    │    └─BatchNorm3d: 3-5             [1, 20, 8, 26, 39]        40\n",
              "│    └─ReLU: 2-6                         [1, 20, 8, 26, 39]        --\n",
              "│    └─Sequential: 2-7                   [1, 20, 5, 14, 20]        --\n",
              "│    │    └─Conv3d: 3-6                  [1, 20, 5, 14, 20]        25,600\n",
              "│    │    └─BatchNorm3d: 3-7             [1, 20, 5, 14, 20]        40\n",
              "│    │    └─ReLU: 3-8                    [1, 20, 5, 14, 20]        --\n",
              "│    └─Sequential: 2-8                   [1, 20, 3, 8, 11]         --\n",
              "│    │    └─Conv3d: 3-9                  [1, 20, 3, 8, 11]         25,600\n",
              "│    │    └─BatchNorm3d: 3-10            [1, 20, 3, 8, 11]         40\n",
              "│    └─ReLU: 2-9                         [1, 20, 3, 8, 11]         --\n",
              "├─Sequential: 1-3                        [1, 40, 2, 2, 2]          --\n",
              "│    └─Sequential: 2-10                  [1, 40, 2, 5, 6]          --\n",
              "│    │    └─Conv3d: 3-11                 [1, 40, 2, 5, 6]          51,200\n",
              "│    │    └─BatchNorm3d: 3-12            [1, 40, 2, 5, 6]          80\n",
              "│    │    └─ReLU: 3-13                   [1, 40, 2, 5, 6]          --\n",
              "│    └─Sequential: 2-11                  [1, 40, 2, 3, 4]          --\n",
              "│    │    └─Conv3d: 3-14                 [1, 40, 2, 3, 4]          102,400\n",
              "│    │    └─BatchNorm3d: 3-15            [1, 40, 2, 3, 4]          80\n",
              "│    └─ReLU: 2-12                        [1, 40, 2, 3, 4]          --\n",
              "│    └─Sequential: 2-13                  [1, 40, 2, 2, 3]          --\n",
              "│    │    └─Conv3d: 3-16                 [1, 40, 2, 2, 3]          102,400\n",
              "│    │    └─BatchNorm3d: 3-17            [1, 40, 2, 2, 3]          80\n",
              "│    │    └─ReLU: 3-18                   [1, 40, 2, 2, 3]          --\n",
              "│    └─Sequential: 2-14                  [1, 40, 2, 2, 2]          --\n",
              "│    │    └─Conv3d: 3-19                 [1, 40, 2, 2, 2]          102,400\n",
              "│    │    └─BatchNorm3d: 3-20            [1, 40, 2, 2, 2]          80\n",
              "│    └─ReLU: 2-15                        [1, 40, 2, 2, 2]          --\n",
              "├─AdaptiveAvgPool3d: 1-4                 [1, 40, 1, 1, 1]          --\n",
              "├─Linear: 1-5                            [1, 11]                   451\n",
              "==========================================================================================\n",
              "Total params: 465,611\n",
              "Trainable params: 465,611\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 3.17\n",
              "==========================================================================================\n",
              "Input size (MB): 36.00\n",
              "Forward/backward pass size (MB): 147.45\n",
              "Params size (MB): 1.86\n",
              "Estimated Total Size (MB): 185.31\n",
              "=========================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and Testing Loops"
      ],
      "metadata": {
        "id": "-3RlF9fAmQS5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "from timeit import default_timer as timer\n",
        "import torchmetrics\n",
        "import datetime"
      ],
      "metadata": {
        "id": "f0LEeHSAmfo6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MAKE TRAIN STEP\n",
        "def train_step(model: torch.nn.Module,\n",
        "               dataloader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               device=device):\n",
        "  model.train()\n",
        "\n",
        "  train_loss, train_acc = 0, 0\n",
        "\n",
        "  for batch, (X, y) in enumerate(dataloader):\n",
        "    X, y = X.to(torch.float32).to(device), y.to(device)\n",
        "\n",
        "    logits = model(X)\n",
        "    loss = loss_fn(logits, y)\n",
        "    train_loss += loss.item()\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    pred_class = torch.softmax(logits, dim=1).argmax()\n",
        "    train_acc += (pred_class==y).sum().item()/len(logits)\n",
        "\n",
        "  train_loss /= len(dataloader)\n",
        "  train_acc /= len(dataloader)\n",
        "  return train_loss, train_acc"
      ],
      "metadata": {
        "id": "4VayVwrOmjEt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MAKE TEST STEP\n",
        "def test_step(model: torch.nn.Module,\n",
        "              dataloader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              device=device):\n",
        "  model.eval()\n",
        "\n",
        "  test_loss, test_acc = 0, 0\n",
        "\n",
        "  with torch.inference_mode():\n",
        "    for batch, (X, y) in enumerate(dataloader):\n",
        "      X, y = X.to(torch.float32).to(device), y.to(device)\n",
        "\n",
        "      logits = model(X)\n",
        "      loss = loss_fn(logits, y)\n",
        "      test_loss += loss.item()\n",
        "\n",
        "      pred_class = torch.softmax(logits, dim=1).argmax()\n",
        "      test_acc += (pred_class==y).sum().item()/len(logits)\n",
        "\n",
        "  test_loss /= len(dataloader)\n",
        "  test_acc /= len(dataloader)\n",
        "  return test_loss, test_acc"
      ],
      "metadata": {
        "id": "EmkgPivsmkm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(model: torch.nn.Module,\n",
        "                epochs: int,\n",
        "                train_dataloader: torch.utils.data.DataLoader,\n",
        "                test_dataloader: torch.utils.data.DataLoader,\n",
        "                loss_fn: torch.nn.Module,\n",
        "                optimizer: torch.optim.Optimizer):\n",
        "  start_time = timer()\n",
        "  store_results = {}\n",
        "\n",
        "  for epoch in tqdm(range(epochs)):\n",
        "    torch.cuda.empty_cache()\n",
        "    train_loss, train_acc = train_step(model=model,\n",
        "                                       dataloader=train_dataloader,\n",
        "                                       loss_fn=loss_fn,\n",
        "                                       optimizer=optimizer,\n",
        "                                       device=device)\n",
        "    torch.cuda.empty_cache()\n",
        "    test_loss, test_acc = test_step(model=model,\n",
        "                                    dataloader=test_dataloader,\n",
        "                                    loss_fn=loss_fn,\n",
        "                                    device=device)\n",
        "\n",
        "    store_results[epoch] = [train_loss, train_acc, test_loss, test_acc]\n",
        "    print(f\"Epoch: {epoch} ----------\")\n",
        "    print(f\"Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f} | Test Loss: {test_loss:.4f} | Test Acc: {test_acc:.4f}\\n\")\n",
        "\n",
        "  end_time = timer()\n",
        "  train_time = datetime.timedelta(seconds = round(end_time-start_time))\n",
        "  print(f\"Total Training Time: {train_time}\")\n",
        "\n",
        "  return store_results, train_time"
      ],
      "metadata": {
        "id": "CunJ5w7BmmSe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train the Model with Different Data Transformations"
      ],
      "metadata": {
        "id": "bQZU1AKTmppk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "os.mkdir('saved_models')"
      ],
      "metadata": {
        "id": "jcKQXbLGqW4h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Custom Dimensions"
      ],
      "metadata": {
        "id": "WOthaMQnnEsz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_dataloaders_custom_dimensions(input_video_path: str,\n",
        "                                      output_video_path: str,\n",
        "                                      BATCH_SIZE: int = 1,\n",
        "                                      NUM_WORKERS: int = 0):\n",
        "  height = int(input('What height for the videos?'))\n",
        "  width = int(input('What width for the videos?'))\n",
        "\n",
        "  #Prep and test the transformations\n",
        "  custom_dimensions_transform = transforms.Compose([ #input is THWC\n",
        "    pytorchvideo.transforms.Permute((0,3,1,2)),\n",
        "    transforms.Resize((height, width)), #takes TCHW\n",
        "    transforms.RandomHorizontalFlip(p=0.5), #takes TCHW\n",
        "    pytorchvideo.transforms.Permute((1,0,2,3)) #return CTHW\n",
        "  ])\n",
        "  print('Testing the transformation before the model runs')\n",
        "  test_transformation(video_path=input_video_path,\n",
        "                      transformation=custom_dimensions_transform,\n",
        "                      output_path=output_video_path)\n",
        "\n",
        "\n",
        "  #Prep the Datasets and DataLoaders\n",
        "  train_dataset_custom = VideoFolder(train_dir, custom_dimensions_transform)\n",
        "  test_dataset_custom = VideoFolder(test_dir, custom_dimensions_transform)\n",
        "  train_dataloader_custom = DataLoader(dataset=train_dataset_custom,\n",
        "                                       batch_size=BATCH_SIZE,\n",
        "                                       num_workers=NUM_WORKERS,\n",
        "                                       shuffle=True)\n",
        "  test_dataloader_custom = DataLoader(dataset=test_dataset_custom,\n",
        "                                      batch_size=BATCH_SIZE,\n",
        "                                      num_workers=NUM_WORKERS,\n",
        "                                      shuffle=False)\n",
        "  return train_dataloader_custom, test_dataloader_custom"
      ],
      "metadata": {
        "id": "bBanYYDWnPYc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader_custom, test_dataloader_custom = get_dataloaders_custom_dimensions(\n",
        "    input_video_path=video_path,\n",
        "    output_video_path='/content/test_transformations/train_custom_transformation_output.mpg')\n",
        "\n",
        "NUM_EPOCHS_custom = 10\n",
        "mini_r3d18_custom = MiniR3D18().to(device)\n",
        "loss_fn_custom = nn.CrossEntropyLoss()\n",
        "optimizer_custom = torch.optim.SGD(params=mini_r3d18_custom.parameters(), lr=0.01)\n",
        "\n",
        "results_custom, train_time_custom = train_model(model = mini_r3d18_custom,\n",
        "                                                epochs=NUM_EPOCHS_custom,\n",
        "                                                train_dataloader=train_dataloader_custom,\n",
        "                                                test_dataloader=test_dataloader_custom,\n",
        "                                                loss_fn=loss_fn_custom,\n",
        "                                                optimizer=optimizer_custom)"
      ],
      "metadata": {
        "id": "ZfZioLVCppQF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748,
          "referenced_widgets": [
            "fcb161276add4a80850d48fa4ecf1bad",
            "1db5c4104f0743c3b0e266ccb1349c34",
            "cc3ee8bc01cc4f69b4b010f1c09a94e2",
            "20d7483612224ffba33c99321549348b",
            "939ed4a815d1469e81cc83356a249ea5",
            "a53486fbaad049448c9e602049e59966",
            "99335251942f42e2a0d8c4698af6d9ee",
            "e077d1f128044ca08ecbbece598a8615",
            "8081cabffcef433487303b66d510d816",
            "3eaaf11119f346c8abfa1e1fce6b2f34",
            "52e5a8664bd04bd88994c166302c9d98"
          ]
        },
        "outputId": "6203c510-bde7-4634-8faa-3f128ca5f1a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What height for the videos?100\n",
            "What width for the videos?100\n",
            "Testing the transformation before the model runs\n",
            "Loaded video shape: torch.Size([135, 240, 320, 3])\n",
            "Transformed video shape: torch.Size([135, 100, 100, 3])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fcb161276add4a80850d48fa4ecf1bad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 ----------\n",
            "Train Loss: 2.3803 | Train Acc: 0.1240 | Test Loss: 2.3413 | Test Acc: 0.1258\n",
            "\n",
            "Epoch: 1 ----------\n",
            "Train Loss: 2.3090 | Train Acc: 0.1617 | Test Loss: 2.2826 | Test Acc: 0.2086\n",
            "\n",
            "Epoch: 2 ----------\n",
            "Train Loss: 2.2397 | Train Acc: 0.2119 | Test Loss: 2.2154 | Test Acc: 0.2239\n",
            "\n",
            "Epoch: 3 ----------\n",
            "Train Loss: 2.1732 | Train Acc: 0.2488 | Test Loss: 2.2021 | Test Acc: 0.2362\n",
            "\n",
            "Epoch: 4 ----------\n",
            "Train Loss: 2.1288 | Train Acc: 0.2488 | Test Loss: 2.1314 | Test Acc: 0.2393\n",
            "\n",
            "Epoch: 5 ----------\n",
            "Train Loss: 2.0700 | Train Acc: 0.2567 | Test Loss: 2.0564 | Test Acc: 0.2546\n",
            "\n",
            "Epoch: 6 ----------\n",
            "Train Loss: 2.0356 | Train Acc: 0.2590 | Test Loss: 2.0107 | Test Acc: 0.2607\n",
            "\n",
            "Epoch: 7 ----------\n",
            "Train Loss: 2.0072 | Train Acc: 0.2826 | Test Loss: 2.0390 | Test Acc: 0.2730\n",
            "\n",
            "Epoch: 8 ----------\n",
            "Train Loss: 1.9724 | Train Acc: 0.2818 | Test Loss: 2.0007 | Test Acc: 0.2362\n",
            "\n",
            "Epoch: 9 ----------\n",
            "Train Loss: 1.9114 | Train Acc: 0.2920 | Test Loss: 1.9374 | Test Acc: 0.3190\n",
            "\n",
            "Total Training Time: 0:58:49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#save the model\n",
        "model_custom_path = '/content/saved_models/model_custom_dimensions.pth'\n",
        "torch.save(obj=mini_r3d18_custom.state_dict(), f=model_custom_path)"
      ],
      "metadata": {
        "id": "7lA-tIeIqb1t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0.1 Proportion Crop Transformation"
      ],
      "metadata": {
        "id": "4rPmGjyl7SZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create the parameters\n",
        "NUM_EPOCHS_1proportion = 10\n",
        "mini_r3d18_1proportion = MiniR3D18().to(device)\n",
        "loss_fn_1proportion = nn.CrossEntropyLoss()\n",
        "optimizer_1proportion = torch.optim.SGD(params=mini_r3d18_1proportion.parameters(), lr=0.01)\n",
        "\n",
        "#run the model\n",
        "results_1proportion, train_time_1proportion = train_model(\n",
        "    model=mini_r3d18_1proportion,\n",
        "    epochs=NUM_EPOCHS_1proportion,\n",
        "    train_dataloader=train_dataloader_1proportion,\n",
        "    test_dataloader=test_dataloader_1proportion,\n",
        "    loss_fn=loss_fn_1proportion,\n",
        "    optimizer=optimizer_1proportion)\n",
        "\n",
        "#save the model\n",
        "model_1proportion_path = '/content/saved_models/model_1proportion.pth'\n",
        "torch.save(obj=mini_r3d18_1proportion.state_dict(), f=model_1proportion_path)"
      ],
      "metadata": {
        "id": "NmJu6zrU7V71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603,
          "referenced_widgets": [
            "88899e82111442bbb2e77b4ce77cb1a7",
            "792939cd00364b50862c647883ca7e79",
            "7644be87f94d42748e6c380662c3bcb4",
            "1fcb89a31ecd4f79be86398b85898cd1",
            "78134ff8c1cc4606a933359ab13d25de",
            "b03de0b3149343658905c5f9cea2864a",
            "db511e66706d47d9ad0f50717fb56bb6",
            "cdc5e75123c64bcc845cff0383cced8f",
            "0b57147bdb9a4cdba3c8a8aab306c355",
            "eb759b0ce9aa478ca3698713fcb18490",
            "c9f5373bd31e4fb38aa724fba87b8a4e"
          ]
        },
        "outputId": "fc60cb7f-f451-455e-c6a9-a1e2fc18fbde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88899e82111442bbb2e77b4ce77cb1a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 ----------\n",
            "Train Loss: 2.3737 | Train Acc: 0.1523 | Test Loss: 2.3419 | Test Acc: 0.1933\n",
            "\n",
            "Epoch: 1 ----------\n",
            "Train Loss: 2.2835 | Train Acc: 0.2072 | Test Loss: 2.2156 | Test Acc: 0.2393\n",
            "\n",
            "Epoch: 2 ----------\n",
            "Train Loss: 2.2090 | Train Acc: 0.2268 | Test Loss: 2.2092 | Test Acc: 0.2393\n",
            "\n",
            "Epoch: 3 ----------\n",
            "Train Loss: 2.1662 | Train Acc: 0.2410 | Test Loss: 2.1136 | Test Acc: 0.2761\n",
            "\n",
            "Epoch: 4 ----------\n",
            "Train Loss: 2.0603 | Train Acc: 0.3038 | Test Loss: 1.9887 | Test Acc: 0.3221\n",
            "\n",
            "Epoch: 5 ----------\n",
            "Train Loss: 1.9614 | Train Acc: 0.3446 | Test Loss: 1.9073 | Test Acc: 0.3497\n",
            "\n",
            "Epoch: 6 ----------\n",
            "Train Loss: 1.8213 | Train Acc: 0.3925 | Test Loss: 1.8380 | Test Acc: 0.3712\n",
            "\n",
            "Epoch: 7 ----------\n",
            "Train Loss: 1.6519 | Train Acc: 0.4686 | Test Loss: 1.6093 | Test Acc: 0.5061\n",
            "\n",
            "Epoch: 8 ----------\n",
            "Train Loss: 1.4829 | Train Acc: 0.5157 | Test Loss: 1.5008 | Test Acc: 0.4908\n",
            "\n",
            "Epoch: 9 ----------\n",
            "Train Loss: 1.2722 | Train Acc: 0.6036 | Test Loss: 1.2825 | Test Acc: 0.5399\n",
            "\n",
            "Total Training Time: 1:26:27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Graph the Results"
      ],
      "metadata": {
        "id": "63p_lkFqq-bv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "-q3ffg6wrBAm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns = ['Train Loss', 'Train Accuracy', 'Test Loss', 'Test Accuracy']\n",
        "\n",
        "#AS WE TRAIN MORE, MAKE MORE DATAFRAMES AND ADD TO THE DICTIONARY LISTS\n",
        "\n",
        "df_custom = pd.DataFrame.from_dict(\n",
        "    results_custom,\n",
        "    orient='index',\n",
        "    columns=columns)\n",
        "df_1proportion = pd.DataFrame.from_dict(\n",
        "    results_1proportion,\n",
        "    orient='index',\n",
        "    columns=columns)\n",
        "\n",
        "graph_dict = {'Names': ['Custom', '0.1 Proportion Cropped'],\n",
        "              'Epochs': [NUM_EPOCHS_custom, NUM_EPOCHS_1proportion],\n",
        "              'DataFrames': [df_custom, df_1proportion],\n",
        "              'Times': [train_time_custom.seconds, train_time_1proportion.seconds]}"
      ],
      "metadata": {
        "id": "dMbe4rFDrCEQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "for i, label in enumerate(columns):\n",
        "  plt.subplot(2,2,i+1)\n",
        "\n",
        "  for j, name in enumerate(graph_dict['Names']):\n",
        "    plt.plot(range(1, graph_dict['Epochs'][j]+1),\n",
        "             graph_dict['DataFrames'][j][label].tolist(),\n",
        "             label = name)\n",
        "\n",
        "  plt.title(f\"{label} Curves\")\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.legend()\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "for i, time in enumerate(graph_dict['Times']):\n",
        "  graph_dict['Times'][i] = time / 3600\n",
        "plt.bar(graph_dict['Names'], graph_dict['Times'])\n",
        "plt.ylabel(\"Time (Hours)\")\n",
        "plt.title(\"Model Training Time on Different Data\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "koWwXarqrRFd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2bc3f1a7-68f3-40be-8129-eba34b4fd055"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}