movinet_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v5XUp6LhLgDT",
    "outputId": "21511c9f-d621-4b7b-b47c-222cbcc047ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting av\n",
      "  Downloading av-8.0.3-cp37-cp37m-manylinux2010_x86_64.whl (37.2 MB)\n",
      "\u001b[K     |████████████████████████████████| 37.2 MB 28 kB/s \n",
      "\u001b[?25hInstalling collected packages: av\n",
      "Successfully installed av-8.0.3\n",
      "--2021-07-30 12:32:33--  https://raw.githubusercontent.com/pytorch/vision/6de158c473b83cf43344a0651d7c01128c7850e6/references/video_classification/transforms.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3102 (3.0K) [text/plain]\n",
      "Saving to: ‘transforms.py’\n",
      "\n",
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      "\n",
      "2021-07-30 12:32:33 (48.4 MB/s) - ‘transforms.py’ saved [3102/3102]\n",
      "\n",
      "--2021-07-30 12:32:34--  http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar\n",
      "Resolving serre-lab.clps.brown.edu (serre-lab.clps.brown.edu)... 128.148.254.114\n",
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      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar [following]\n",
      "--2021-07-30 12:32:34--  https://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar\n",
      "Connecting to serre-lab.clps.brown.edu (serre-lab.clps.brown.edu)|128.148.254.114|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2124008126 (2.0G)\n",
      "Saving to: ‘hmdb51_org.rar’\n",
      "\n",
      "hmdb51_org.rar      100%[===================>]   1.98G  38.9MB/s    in 55s     \n",
      "\n",
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      "\n",
      "--2021-07-30 12:33:29--  http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/test_train_splits.rar\n",
      "Resolving serre-lab.clps.brown.edu (serre-lab.clps.brown.edu)... 128.148.254.114\n",
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      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/test_train_splits.rar [following]\n",
      "--2021-07-30 12:33:30--  https://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/test_train_splits.rar\n",
      "Connecting to serre-lab.clps.brown.edu (serre-lab.clps.brown.edu)|128.148.254.114|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 199521 (195K)\n",
      "Saving to: ‘test_train_splits.rar’\n",
      "\n",
      "test_train_splits.r 100%[===================>] 194.84K   688KB/s    in 0.3s    \n",
      "\n",
      "2021-07-30 12:33:30 (688 KB/s) - ‘test_train_splits.rar’ saved [199521/199521]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!pip install av\n",
    "! wget https://raw.githubusercontent.com/pytorch/vision/6de158c473b83cf43344a0651d7c01128c7850e6/references/video_classification/transforms.py\n",
    "# Download HMDB51 data and splits from serre lab website\n",
    "! wget http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar\n",
    "! wget http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/test_train_splits.rar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XEu2JhJK0sO7",
    "outputId": "af81911f-68ba-4afb-ebc5-c490a38d221b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/Atze00/MoViNet-pytorch.git@v0.2\n",
      "  Cloning https://github.com/Atze00/MoViNet-pytorch.git (to revision v0.2) to /tmp/pip-req-build-1oz6sjw9\n",
      "  Running command git clone -q https://github.com/Atze00/MoViNet-pytorch.git /tmp/pip-req-build-1oz6sjw9\n",
      "  Running command git checkout -b v0.2 --track origin/v0.2\n",
      "  Switched to a new branch 'v0.2'\n",
      "  Branch 'v0.2' set up to track remote branch 'v0.2' from 'origin'.\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from MoViNet-pytorch==0.2.0) (1.9.0+cu102)\n",
      "Collecting fvcore\n",
      "  Downloading fvcore-0.1.5.post20210730.tar.gz (49 kB)\n",
      "\u001b[K     |████████████████████████████████| 49 kB 3.7 MB/s \n",
      "\u001b[?25hCollecting einops\n",
      "  Downloading einops-0.3.0-py2.py3-none-any.whl (25 kB)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from fvcore->MoViNet-pytorch==0.2.0) (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 13.3 MB/s \n",
      "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from fvcore->MoViNet-pytorch==0.2.0) (4.41.1)\n",
      "Requirement already satisfied: termcolor>=1.1 in /usr/local/lib/python3.7/dist-packages (from fvcore->MoViNet-pytorch==0.2.0) (1.1.0)\n",
      "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from fvcore->MoViNet-pytorch==0.2.0) (7.1.2)\n",
      "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from fvcore->MoViNet-pytorch==0.2.0) (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-2.3.0-py2.py3-none-any.whl (15 kB)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->MoViNet-pytorch==0.2.0) (3.7.4.3)\n",
      "Building wheels for collected packages: MoViNet-pytorch, fvcore\n",
      "  Building wheel for MoViNet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for MoViNet-pytorch: filename=MoViNet_pytorch-0.2.0-py3-none-any.whl size=12025 sha256=daf5155db8c8b150e912021d5aa479ab2ef45bbd3aa5f9c8ae09a87ba9805367\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-7vrz8xc3/wheels/59/99/a5/43c18d233e06a21774b6494a17871b37edf6d31308ae0fb822\n",
      "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for fvcore: filename=fvcore-0.1.5.post20210730-py3-none-any.whl size=60619 sha256=af28fe7c58e39f1f3bf2b06ab696ffe92e3233461dece85520c1993e31a6773c\n",
      "  Stored in directory: /root/.cache/pip/wheels/fc/9c/12/9e9331cff3ddb5bb02a818886f0b52f5d160c54efa19d3516a\n",
      "Successfully built MoViNet-pytorch fvcore\n",
      "Installing collected packages: pyyaml, portalocker, yacs, iopath, fvcore, einops, MoViNet-pytorch\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 MoViNet-pytorch-0.2.0 einops-0.3.0 fvcore-0.1.5.post20210730 iopath-0.1.9 portalocker-2.3.0 pyyaml-5.4.1 yacs-0.1.8\n"
     ]
    }
   ],
   "source": [
    "pip install git+https://github.com/Atze00/MoViNet-pytorch.git\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IdPFmCYjgviY",
    "outputId": "2820e9e1-f348-43ed-b328-0326c0ed0ab7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "UNRAR 5.50 freeware      Copyright (c) 1993-2017 Alexander Roshal\n",
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      "Extracting  test_train_splits/push_test_split3.txt                       \b\b\b\b 55%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/ride_bike_test_split1.txt                  \b\b\b\b 55%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/ride_bike_test_split2.txt                  \b\b\b\b 56%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/ride_bike_test_split3.txt                  \b\b\b\b 56%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/ride_horse_test_split1.txt                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/ride_horse_test_split2.txt                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/ride_horse_test_split3.txt                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/run_test_split1.txt                        \b\b\b\b 58%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/run_test_split2.txt                        \b\b\b\b 59%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/run_test_split3.txt                        \b\b\b\b 60%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shake_hands_test_split1.txt                \b\b\b\b 62%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shake_hands_test_split2.txt                \b\b\b\b 63%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shake_hands_test_split3.txt                \b\b\b\b 64%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_ball_test_split1.txt                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_ball_test_split2.txt                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_ball_test_split3.txt                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_bow_test_split1.txt                  \b\b\b\b 66%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_bow_test_split2.txt                  \b\b\b\b 66%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_bow_test_split3.txt                  \b\b\b\b 67%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_gun_test_split1.txt                  \b\b\b\b 67%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_gun_test_split2.txt                  \b\b\b\b 67%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/shoot_gun_test_split3.txt                  \b\b\b\b 68%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/situp_test_split1.txt                      \b\b\b\b 69%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/situp_test_split2.txt                      \b\b\b\b 69%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/situp_test_split3.txt                      \b\b\b\b 70%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sit_test_split1.txt                        \b\b\b\b 71%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sit_test_split2.txt                        \b\b\b\b 71%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sit_test_split3.txt                        \b\b\b\b 72%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/smile_test_split1.txt                      \b\b\b\b 73%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/smile_test_split2.txt                      \b\b\b\b 73%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/smile_test_split3.txt                      \b\b\b\b 73%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/smoke_test_split1.txt                      \b\b\b\b 74%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/smoke_test_split2.txt                      \b\b\b\b 74%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/smoke_test_split3.txt                      \b\b\b\b 75%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/somersault_test_split1.txt                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/somersault_test_split2.txt                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/somersault_test_split3.txt                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/stand_test_split1.txt                      \b\b\b\b 78%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/stand_test_split2.txt                      \b\b\b\b 79%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/stand_test_split3.txt                      \b\b\b\b 80%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/swing_baseball_test_split1.txt             \b\b\b\b 80%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/swing_baseball_test_split2.txt             \b\b\b\b 81%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/swing_baseball_test_split3.txt             \b\b\b\b 81%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sword_exercise_test_split1.txt             \b\b\b\b 82%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sword_exercise_test_split2.txt             \b\b\b\b 83%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sword_exercise_test_split3.txt             \b\b\b\b 83%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sword_test_split1.txt                      \b\b\b\b 84%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sword_test_split2.txt                      \b\b\b\b 85%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/sword_test_split3.txt                      \b\b\b\b 86%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/talk_test_split1.txt                       \b\b\b\b 86%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/talk_test_split2.txt                       \b\b\b\b 87%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/talk_test_split3.txt                       \b\b\b\b 87%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/throw_test_split1.txt                      \b\b\b\b 88%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/throw_test_split2.txt                      \b\b\b\b 88%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/throw_test_split3.txt                      \b\b\b\b 89%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/turn_test_split1.txt                       \b\b\b\b 90%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/turn_test_split2.txt                       \b\b\b\b 91%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/turn_test_split3.txt                       \b\b\b\b 92%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/walk_test_split1.txt                       \b\b\b\b 94%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/walk_test_split2.txt                       \b\b\b\b 95%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/walk_test_split3.txt                       \b\b\b\b 97%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/wave_test_split1.txt                       \b\b\b\b 98%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/wave_test_split2.txt                       \b\b\b\b 99%\b\b\b\b\b  OK \n",
      "Extracting  test_train_splits/wave_test_split3.txt                       \b\b\b\b 99%\b\b\b\b\b  OK \n",
      "All OK\n",
      "\n",
      "UNRAR 5.50 freeware      Copyright (c) 1993-2017 Alexander Roshal\n",
      "\n",
      "\n",
      "Extracting from hmdb51_org.rar\n",
      "\n",
      "Extracting  shoot_gun.rar                                                \b\b\b\b  0%\b\b\b\b  1%\b\b\b\b\b  OK \n",
      "Extracting  sit.rar                                                      \b\b\b\b  1%\b\b\b\b  2%\b\b\b\b  3%\b\b\b\b\b  OK \n",
      "Extracting  situp.rar                                                    \b\b\b\b  3%\b\b\b\b  4%\b\b\b\b\b  OK \n",
      "Extracting  smile.rar                                                    \b\b\b\b  4%\b\b\b\b  5%\b\b\b\b\b  OK \n",
      "Extracting  smoke.rar                                                    \b\b\b\b  5%\b\b\b\b  6%\b\b\b\b  7%\b\b\b\b\b  OK \n",
      "Extracting  somersault.rar                                               \b\b\b\b  7%\b\b\b\b  8%\b\b\b\b  9%\b\b\b\b\b  OK \n",
      "Extracting  stand.rar                                                    \b\b\b\b  9%\b\b\b\b 10%\b\b\b\b 11%\b\b\b\b\b  OK \n",
      "Extracting  swing_baseball.rar                                           \b\b\b\b 11%\b\b\b\b 12%\b\b\b\b 13%\b\b\b\b\b  OK \n",
      "Extracting  sword.rar                                                    \b\b\b\b 13%\b\b\b\b 14%\b\b\b\b 15%\b\b\b\b\b  OK \n",
      "Extracting  sword_exercise.rar                                           \b\b\b\b 15%\b\b\b\b 16%\b\b\b\b 17%\b\b\b\b\b  OK \n",
      "Extracting  talk.rar                                                     \b\b\b\b 17%\b\b\b\b 18%\b\b\b\b 19%\b\b\b\b\b  OK \n",
      "Extracting  throw.rar                                                    \b\b\b\b 19%\b\b\b\b 20%\b\b\b\b 21%\b\b\b\b\b  OK \n",
      "Extracting  turn.rar                                                     \b\b\b\b 21%\b\b\b\b 22%\b\b\b\b 23%\b\b\b\b\b  OK \n",
      "Extracting  walk.rar                                                     \b\b\b\b 23%\b\b\b\b 24%\b\b\b\b 25%\b\b\b\b 26%\b\b\b\b 27%\b\b\b\b 28%\b\b\b\b 29%\b\b\b\b 30%\b\b\b\b 31%\b\b\b\b 32%\b\b\b\b\b  OK \n",
      "Extracting  wave.rar                                                     \b\b\b\b 32%\b\b\b\b 33%\b\b\b\b\b  OK \n",
      "Extracting  brush_hair.rar                                               \b\b\b\b 33%\b\b\b\b 34%\b\b\b\b 35%\b\b\b\b 36%\b\b\b\b 37%\b\b\b\b\b  OK \n",
      "Extracting  cartwheel.rar                                                \b\b\b\b 37%\b\b\b\b 38%\b\b\b\b 39%\b\b\b\b\b  OK \n",
      "Extracting  catch.rar                                                    \b\b\b\b 39%\b\b\b\b\b  OK \n",
      "Extracting  chew.rar                                                     \b\b\b\b 39%\b\b\b\b 40%\b\b\b\b 41%\b\b\b\b\b  OK \n",
      "Extracting  clap.rar                                                     \b\b\b\b 41%\b\b\b\b 42%\b\b\b\b 43%\b\b\b\b\b  OK \n",
      "Extracting  climb.rar                                                    \b\b\b\b 43%\b\b\b\b 44%\b\b\b\b 45%\b\b\b\b\b  OK \n",
      "Extracting  climb_stairs.rar                                             \b\b\b\b 45%\b\b\b\b 46%\b\b\b\b 47%\b\b\b\b\b  OK \n",
      "Extracting  dive.rar                                                     \b\b\b\b 47%\b\b\b\b 48%\b\b\b\b\b  OK \n",
      "Extracting  draw_sword.rar                                               \b\b\b\b 49%\b\b\b\b 50%\b\b\b\b\b  OK \n",
      "Extracting  dribble.rar                                                  \b\b\b\b 50%\b\b\b\b 51%\b\b\b\b 52%\b\b\b\b\b  OK \n",
      "Extracting  drink.rar                                                    \b\b\b\b 52%\b\b\b\b 53%\b\b\b\b 54%\b\b\b\b\b  OK \n",
      "Extracting  eat.rar                                                      \b\b\b\b 54%\b\b\b\b 55%\b\b\b\b\b  OK \n",
      "Extracting  fall_floor.rar                                               \b\b\b\b 55%\b\b\b\b 56%\b\b\b\b 57%\b\b\b\b\b  OK \n",
      "Extracting  fencing.rar                                                  \b\b\b\b 57%\b\b\b\b 58%\b\b\b\b\b  OK \n",
      "Extracting  flic_flac.rar                                                \b\b\b\b 58%\b\b\b\b 59%\b\b\b\b 60%\b\b\b\b\b  OK \n",
      "Extracting  golf.rar                                                     \b\b\b\b 60%\b\b\b\b 61%\b\b\b\b\b  OK \n",
      "Extracting  handstand.rar                                                \b\b\b\b 61%\b\b\b\b 62%\b\b\b\b 63%\b\b\b\b\b  OK \n",
      "Extracting  hit.rar                                                      \b\b\b\b 63%\b\b\b\b 64%\b\b\b\b\b  OK \n",
      "Extracting  hug.rar                                                      \b\b\b\b 64%\b\b\b\b 65%\b\b\b\b\b  OK \n",
      "Extracting  jump.rar                                                     \b\b\b\b 65%\b\b\b\b 66%\b\b\b\b 67%\b\b\b\b\b  OK \n",
      "Extracting  kick.rar                                                     \b\b\b\b 67%\b\b\b\b 68%\b\b\b\b\b  OK \n",
      "Extracting  kick_ball.rar                                                \b\b\b\b 68%\b\b\b\b 69%\b\b\b\b\b  OK \n",
      "Extracting  kiss.rar                                                     \b\b\b\b 69%\b\b\b\b 70%\b\b\b\b 71%\b\b\b\b\b  OK \n",
      "Extracting  laugh.rar                                                    \b\b\b\b 71%\b\b\b\b 72%\b\b\b\b 73%\b\b\b\b 74%\b\b\b\b 75%\b\b\b\b\b  OK \n",
      "Extracting  pick.rar                                                     \b\b\b\b 75%\b\b\b\b 76%\b\b\b\b\b  OK \n",
      "Extracting  pour.rar                                                     \b\b\b\b 76%\b\b\b\b 77%\b\b\b\b 78%\b\b\b\b\b  OK \n",
      "Extracting  pullup.rar                                                   \b\b\b\b 78%\b\b\b\b 79%\b\b\b\b 80%\b\b\b\b\b  OK \n",
      "Extracting  punch.rar                                                    \b\b\b\b 80%\b\b\b\b 81%\b\b\b\b\b  OK \n",
      "Extracting  push.rar                                                     \b\b\b\b 81%\b\b\b\b 82%\b\b\b\b 83%\b\b\b\b\b  OK \n",
      "Extracting  pushup.rar                                                   \b\b\b\b 83%\b\b\b\b 84%\b\b\b\b\b  OK \n",
      "Extracting  ride_bike.rar                                                \b\b\b\b 84%\b\b\b\b 85%\b\b\b\b 86%\b\b\b\b\b  OK \n",
      "Extracting  ride_horse.rar                                               \b\b\b\b 86%\b\b\b\b 87%\b\b\b\b 88%\b\b\b\b 89%\b\b\b\b\b  OK \n",
      "Extracting  run.rar                                                      \b\b\b\b 89%\b\b\b\b 90%\b\b\b\b 91%\b\b\b\b 92%\b\b\b\b\b  OK \n",
      "Extracting  shake_hands.rar                                              \b\b\b\b 92%\b\b\b\b 93%\b\b\b\b 94%\b\b\b\b 95%\b\b\b\b\b  OK \n",
      "Extracting  shoot_ball.rar                                               \b\b\b\b 95%\b\b\b\b 96%\b\b\b\b\b  OK \n",
      "Extracting  shoot_bow.rar                                                \b\b\b\b 96%\b\b\b\b 97%\b\b\b\b 98%\b\b\b\b 99%\b\b\b\b\b  OK \n",
      "All OK\n"
     ]
    }
   ],
   "source": [
    "# Extract and organize video data..\n",
    "! mkdir -p video_data test_train_splits\n",
    "! unrar e test_train_splits.rar test_train_splits\n",
    "! rm test_train_splits.rar\n",
    "! unrar e hmdb51_org.rar \n",
    "! rm hmdb51_org.rar\n",
    "! mv *.rar video_data\n",
    "import os\n",
    "for files in os.listdir('video_data'):\n",
    "    foldername = files.split('.')[0]\n",
    "    os.system(\"mkdir -p video_data/\" + foldername)\n",
    "    os.system(\"unrar e video_data/\"+ files + \" video_data/\"+foldername)\n",
    "\n",
    "! rm video_data/*.rar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170,
     "referenced_widgets": [
      "d7162af7b4084a999cc709307e40616a",
      "6db30d527fa94b16a6af19e80828d0e6",
      "f5cfea9b48b04fef9df36d118efd1722",
      "15fb89fe0f454fd8a47316c5f4fe50a9",
      "3c70c93bde99420c84c0ad818b2dd7c6",
      "4f419af9ec4e4605b7a5404dee09e33c",
      "fb81268f11db41f4a09cad4206a4e04a",
      "10cf8cfbba5d408f905855995c502fba",
      "df50195d063b402f93d8552336045c3c",
      "39ca1a3fed5c4067a020bd7027b9fbf1",
      "e74d503d808e4c5da75eff1d096590ad",
      "d2724859dc7e48b29fcdb0ab75c75675",
      "56c1bc4a801c41c29dbfb00237703ef4",
      "acb87c19a53c40c5b74353d2ac6f0de6",
      "7543a992a3274c5b944f31361d4ddc4d",
      "7e42e2ea9cad463c8ebe2a54801218b2"
     ]
    },
    "id": "JpChe2ZX2-Zq",
    "outputId": "9b989e52-7ea4-4055-8937-4d2801af94dc"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7162af7b4084a999cc709307e40616a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=423.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/video_utils.py:215: UserWarning: There aren't enough frames in the current video to get a clip for the given clip length and frames between clips. The video (and potentially others) will be skipped.\n",
      "  warnings.warn(\"There aren't enough frames in the current video to get a clip for the given clip length and \"\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df50195d063b402f93d8552336045c3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=423.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import torchvision\n",
    "import torch.nn.functional as F\n",
    "import torchvision.transforms as transforms\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import random_split, DataLoader\n",
    "import torch\n",
    "import transforms as T\n",
    "from movinets import MoViNet\n",
    "from movinets.config import _C\n",
    "\n",
    "torch.manual_seed(97)\n",
    "num_frames = 16 # 16\n",
    "clip_steps = 2\n",
    "Bs_Train = 16\n",
    "Bs_Test = 16\n",
    "\n",
    "transform = transforms.Compose([  \n",
    "                                                     \n",
    "                                 T.ToFloatTensorInZeroOne(),\n",
    "                                 T.Resize((200, 200)),\n",
    "                                 T.RandomHorizontalFlip(),\n",
    "                                 #T.Normalize(mean=[0.43216, 0.394666, 0.37645], std=[0.22803, 0.22145, 0.216989]),\n",
    "                                 T.RandomCrop((172, 172))])\n",
    "transform_test = transforms.Compose([                           \n",
    "                                 T.ToFloatTensorInZeroOne(),\n",
    "                                 T.Resize((200, 200)),\n",
    "                                 #T.Normalize(mean=[0.43216, 0.394666, 0.37645], std=[0.22803, 0.22145, 0.216989]),\n",
    "                                 T.CenterCrop((172, 172))])\n",
    "\n",
    "hmdb51_train = torchvision.datasets.HMDB51('video_data/', 'test_train_splits/', num_frames,frame_rate=5,\n",
    "                                                step_between_clips = clip_steps, fold=1, train=True,\n",
    "                                                transform=transform, num_workers=2)\n",
    "\n",
    "\n",
    "hmdb51_test = torchvision.datasets.HMDB51('video_data/', 'test_train_splits/', num_frames,frame_rate=5,\n",
    "                                                step_between_clips = clip_steps, fold=1, train=False,\n",
    "                                                transform=transform_test, num_workers=2)\n",
    "train_loader = DataLoader(hmdb51_train, batch_size=Bs_Train, shuffle=True)\n",
    "test_loader  = DataLoader(hmdb51_test, batch_size=Bs_Test, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "wl_kFUbVLDJb"
   },
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   "source": [
    "def train_iter(model, optimz, data_load, loss_val):\n",
    "    samples = len(data_load.dataset)\n",
    "    model.train()\n",
    "    model.cuda()\n",
    "    model.clean_activation_buffers()\n",
    "    optimz.zero_grad()\n",
    "    for i, (data,_ , target) in enumerate(data_load):\n",
    "        out = F.log_softmax(model(data.cuda()), dim=1)\n",
    "        loss = F.nll_loss(out, target.cuda())\n",
    "        loss.backward()\n",
    "        optimz.step()\n",
    "        optimz.zero_grad()\n",
    "        model.clean_activation_buffers()\n",
    "        if i % 50 == 0:\n",
    "            print('[' +  '{:5}'.format(i * len(data)) + '/' + '{:5}'.format(samples) +\n",
    "                  ' (' + '{:3.0f}'.format(100 * i / len(data_load)) + '%)]  Loss: ' +\n",
    "                  '{:6.4f}'.format(loss.item()))\n",
    "            loss_val.append(loss.item())\n",
    " \n",
    "def evaluate(model, data_load, loss_val):\n",
    "    model.eval()\n",
    "    \n",
    "    samples = len(data_load.dataset)\n",
    "    csamp = 0\n",
    "    tloss = 0\n",
    "    model.clean_activation_buffers()\n",
    "    with torch.no_grad():\n",
    "        for data, _, target in data_load:\n",
    "            output = F.log_softmax(model(data.cuda()), dim=1)\n",
    "            loss = F.nll_loss(output, target.cuda(), reduction='sum')\n",
    "            _, pred = torch.max(output, dim=1)\n",
    "            \n",
    "            tloss += loss.item()\n",
    "            csamp += pred.eq(target.cuda()).sum()\n",
    "            model.clean_activation_buffers()\n",
    "    aloss = tloss / samples\n",
    "    loss_val.append(aloss)\n",
    "    print('\\nAverage test loss: ' + '{:.4f}'.format(aloss) +\n",
    "          '  Accuracy:' + '{:5}'.format(csamp) + '/' +\n",
    "          '{:5}'.format(samples) + ' (' +\n",
    "          '{:4.2f}'.format(100.0 * csamp / samples) + '%)\\n')\n",
    "    \n",
    "def train_iter_stream(model, optimz, data_load, loss_val, n_clips = 2, n_clip_frames=8):\n",
    "    \"\"\"\n",
    "    In causal mode with stream buffer a single video is fed to the network\n",
    "    using subclips of lenght n_clip_frames. \n",
    "    n_clips*n_clip_frames should be equal to the total number of frames presents\n",
    "    in the video.\n",
    "    \n",
    "    n_clips : number of clips that are used\n",
    "    n_clip_frames : number of frame contained in each clip\n",
    "    \"\"\"\n",
    "    #clean the buffer of activations\n",
    "    samples = len(data_load.dataset)\n",
    "    model.cuda()\n",
    "    model.train()\n",
    "    model.clean_activation_buffers()\n",
    "    optimz.zero_grad()\n",
    "    \n",
    "    for i, (data,_, target) in enumerate(data_load):\n",
    "        data = data.cuda()\n",
    "        target = target.cuda()\n",
    "        l_batch = 0\n",
    "        #backward pass for each clip\n",
    "        for j in range(n_clips):\n",
    "          output = F.log_softmax(model(data[:,:,(n_clip_frames)*(j):(n_clip_frames)*(j+1)]), dim=1)\n",
    "          loss = F.nll_loss(output, target)\n",
    "          _, pred = torch.max(output, dim=1)\n",
    "          loss = F.nll_loss(output, target)/n_clips\n",
    "          loss.backward()\n",
    "        l_batch += loss.item()*n_clips\n",
    "        optimz.step()\n",
    "        optimz.zero_grad()\n",
    "        \n",
    "        #clean the buffer of activations\n",
    "        model.clean_activation_buffers()\n",
    "        if i % 50 == 0:\n",
    "            print('[' +  '{:5}'.format(i * len(data)) + '/' + '{:5}'.format(samples) +\n",
    "                  ' (' + '{:3.0f}'.format(100 * i / len(data_load)) + '%)]  Loss: ' +\n",
    "                  '{:6.4f}'.format(l_batch))\n",
    "            loss_val.append(l_batch)\n",
    "\n",
    "def evaluate_stream(model, data_load, loss_val, n_clips = 2, n_clip_frames=8):\n",
    "    model.eval()\n",
    "    model.cuda()\n",
    "    samples = len(data_load.dataset)\n",
    "    csamp = 0\n",
    "    tloss = 0\n",
    "    with torch.no_grad():\n",
    "        for data, _, target in data_load:\n",
    "            data = data.cuda()\n",
    "            target = target.cuda()\n",
    "            model.clean_activation_buffers()\n",
    "            for j in range(n_clips):\n",
    "              output = F.log_softmax(model(data[:,:,(n_clip_frames)*(j):(n_clip_frames)*(j+1)]), dim=1)\n",
    "              loss = F.nll_loss(output, target)\n",
    "            _, pred = torch.max(output, dim=1)\n",
    "            tloss += loss.item()\n",
    "            csamp += pred.eq(target).sum()\n",
    "\n",
    "    aloss = tloss /  len(data_load)\n",
    "    loss_val.append(aloss)\n",
    "    print('\\nAverage test loss: ' + '{:.4f}'.format(aloss) +\n",
    "          '  Accuracy:' + '{:5}'.format(csamp) + '/' +\n",
    "          '{:5}'.format(samples) + ' (' +\n",
    "          '{:4.2f}'.format(100.0 * csamp / samples) + '%)\\n')"
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      "Downloading: \"https://github.com/Atze00/MoViNet-pytorch/blob/main/weights/modelA0_stream_statedict_v3?raw=true\" to /root/.cache/torch/hub/checkpoints/modelA0_stream_statedict_v3\n"
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      "\n",
      "Epoch: 1\n",
      "[    0/ 7519 (  0%)]  Loss: 3.9475\n",
      "[  800/ 7519 ( 11%)]  Loss: 3.2556\n",
      "[ 1600/ 7519 ( 21%)]  Loss: 2.7141\n",
      "[ 2400/ 7519 ( 32%)]  Loss: 2.5516\n",
      "[ 3200/ 7519 ( 43%)]  Loss: 1.7029\n",
      "[ 4000/ 7519 ( 53%)]  Loss: 1.0772\n",
      "[ 4800/ 7519 ( 64%)]  Loss: 1.4322\n",
      "[ 5600/ 7519 ( 74%)]  Loss: 0.6715\n",
      "[ 6400/ 7519 ( 85%)]  Loss: 1.2684\n",
      "[ 7200/ 7519 ( 96%)]  Loss: 1.0167\n",
      "\n",
      "Average test loss: 1.3043  Accuracy: 2147/ 2944 (72.93%)\n",
      "\n",
      "Execution time: 1287.27 seconds\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 1\n",
    "\n",
    "model = MoViNet(_C.MODEL.MoViNetA0, causal = True, pretrained = True )\n",
    "start_time = time.time()\n",
    "\n",
    "trloss_val, tsloss_val = [], []\n",
    "model.classifier[3] = torch.nn.Conv3d(2048, 51, (1,1,1))\n",
    "optimz = optim.Adam(model.parameters(), lr=0.00005)\n",
    "for epoch in range(1, N_EPOCHS + 1):\n",
    "    print('Epoch:', epoch)\n",
    "    train_iter_stream(model, optimz, train_loader, trloss_val)\n",
    "    evaluate_stream(model, test_loader, tsloss_val)\n",
    " \n",
    "print('Execution time:', '{:5.2f}'.format(time.time() - start_time), 'seconds')"
   ]
  },
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   "execution_count": 9,
   "metadata": {
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    "outputId": "3a5e2b8c-9036-4f22-c00e-933f0e092de7"
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     "name": "stdout",
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     "text": [
      "Epoch: 1\n",
      "[    0/ 7519 (  0%)]  Loss: 3.9084\n",
      "[  800/ 7519 ( 11%)]  Loss: 3.5596\n",
      "[ 1600/ 7519 ( 21%)]  Loss: 3.4118\n",
      "[ 2400/ 7519 ( 32%)]  Loss: 2.0215\n",
      "[ 3200/ 7519 ( 43%)]  Loss: 1.8647\n",
      "[ 4000/ 7519 ( 53%)]  Loss: 1.2905\n",
      "[ 4800/ 7519 ( 64%)]  Loss: 1.4333\n",
      "[ 5600/ 7519 ( 74%)]  Loss: 1.5478\n",
      "[ 6400/ 7519 ( 85%)]  Loss: 0.8748\n",
      "[ 7200/ 7519 ( 96%)]  Loss: 1.6601\n",
      "\n",
      "Average test loss: 1.4166  Accuracy: 1893/ 2944 (64.30%)\n",
      "\n",
      "Execution time: 1315.45 seconds\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 1\n",
    "\n",
    "model = MoViNet(_C.MODEL.MoViNetA0, causal = False, pretrained = True )\n",
    "start_time = time.time()\n",
    "\n",
    "trloss_val, tsloss_val = [], []\n",
    "model.classifier[3] = torch.nn.Conv3d(2048, 51, (1,1,1))\n",
    "optimz = optim.Adam(model.parameters(), lr=0.00005)\n",
    "for epoch in range(1, N_EPOCHS + 1):\n",
    "    print('Epoch:', epoch)\n",
    "    train_iter(model, optimz, train_loader, trloss_val)\n",
    "    evaluate(model, test_loader, tsloss_val)\n",
    " \n",
    "print('Execution time:', '{:5.2f}'.format(time.time() - start_time), 'seconds')"
   ]
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   "execution_count": 7,
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