Edit_Fast_PredNet_encoding_model_of_fMRI_data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Edit_Fast PredNet encoding model of fMRI data.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "EdHVQrqyDB50",
        "FgVfBlvZDEja",
        "d6zmir64CXlo",
        "0fNcF4-4Cd-R",
        "2rzGLI22CkfB",
        "HAAFAxHAxfRA",
        "scnuhATSDJX9",
        "jJeWCZ3ODQD7"
      ],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbLBFKq3YZpB"
      },
      "source": [
        "# Stay connected \n",
        "```\n",
        "var startClickConnect = function startClickConnect(){\n",
        "    var clickConnect = function clickConnect(){\n",
        "        console.log(\"Connnect Clicked - Start\");\n",
        "        document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click();\n",
        "        console.log(\"Connnect Clicked - End\"); \n",
        "    };\n",
        "\n",
        "    var intervalId = setInterval(clickConnect, 60000);\n",
        "\n",
        "    var stopClickConnectHandler = function stopClickConnect() {\n",
        "        console.log(\"Connnect Clicked Stopped - Start\");\n",
        "        clearInterval(intervalId);\n",
        "        console.log(\"Connnect Clicked Stopped - End\");\n",
        "    };\n",
        "\n",
        "    return stopClickConnectHandler;\n",
        "};\n",
        "\n",
        "var stopClickConnect = startClickConnect();\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkGSVqt2t_sk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkWPRAA9uA05"
      },
      "source": [
        "Start the execution --> Prednet how does it help us?\n",
        "Videos already added \n",
        "\n",
        "\n",
        "Pending:\n",
        "- Debugging the error\n",
        "- "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdHVQrqyDB50"
      },
      "source": [
        "# Memory Information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E58HaeA6CsM9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db8d5cb5-35d0-49a1-dbab-7e93bbd3bf69"
      },
      "source": [
        "import psutil\n",
        "def get_size(bytes, suffix=\"B\"):\n",
        "    factor = 1024\n",
        "    for unit in [\"\", \"K\", \"M\", \"G\", \"T\", \"P\"]:\n",
        "        if bytes < factor:\n",
        "            return f\"{bytes:.2f}{unit}{suffix}\"\n",
        "        bytes /= factor\n",
        "print(\"=\"*40, \"Memory Information\", \"=\"*40)\n",
        "svmem = psutil.virtual_memory()\n",
        "print(f\"Total: {get_size(svmem.total)}\") ; print(f\"Available: {get_size(svmem.available)}\")\n",
        "print(f\"Used: {get_size(svmem.used)}\") ; print(f\"Percentage: {svmem.percent}%\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "======================================== Memory Information ========================================\n",
            "Total: 12.69GB\n",
            "Available: 11.87GB\n",
            "Used: 580.29MB\n",
            "Percentage: 6.4%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FgVfBlvZDEja"
      },
      "source": [
        "# GPU Information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxlkxvkrCyin",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b81f004-9db0-4499-f9f6-a9cc9c4752a3"
      },
      "source": [
        "! nvidia-smi"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Aug 13 14:34:39 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.42.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P8    28W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6zmir64CXlo"
      },
      "source": [
        "# **Setup**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyDODoaWC6KI"
      },
      "source": [
        "# install libraries not in Colab\n",
        "%%capture\n",
        "!pip install decord\n",
        "!pip install hickle"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEYDrPiVwDQJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a60ebfd8-a373-4117-846c-0a2946fd05e0"
      },
      "source": [
        "!pip install h5py==2.10.0"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: h5py==2.10.0 in /usr/local/lib/python3.7/dist-packages (2.10.0)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.7/dist-packages (from h5py==2.10.0) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py==2.10.0) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IV8HOpt1wFIU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9898729e-8257-4cf4-a8bc-2794b424e4a9"
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "\n",
        "print(tf.__version__)\n",
        "print(keras.__version__)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n",
            "1.15.2\n",
            "2.3.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oo50k7S1wGtF"
      },
      "source": [
        "# standard sci libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "\n",
        "\n",
        "# machine learning libraries\n",
        "from keras import backend as K\n",
        "from keras.legacy.interfaces import generate_legacy_interface, recurrent_args_preprocessor\n",
        "from keras.models import Model, model_from_json\n",
        "from keras import activations\n",
        "from keras.layers import Recurrent\n",
        "from keras.layers import Conv2D, UpSampling2D, MaxPooling2D, Input\n",
        "from keras.engine import InputSpec\n",
        "import theano\n",
        "from torchvision import transforms as trn\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.multioutput import MultiOutputRegressor\n",
        "\n",
        "# image & video libraries\n",
        "from decord import VideoReader  \n",
        "from PIL import Image   \n",
        "\n",
        "# file libraries\n",
        "import glob\n",
        "import os\n",
        "import pickle\n",
        "import hickle\n",
        "import h5py\n",
        "import tqdm"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1smtTLMmCbwG"
      },
      "source": [
        "# random number generators for reproducibility\n",
        "seed = 24\n",
        "\n",
        "tf.random.set_random_seed(seed)\n",
        "np.random.seed(seed)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fNcF4-4Cd-R"
      },
      "source": [
        "# **Data loading**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htBGNx62Cdph",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21775da3-b530-4017-dc3a-3e7d598fd6ff"
      },
      "source": [
        "# download dataset from dropbox\n",
        "dropbox_link = 'https://www.dropbox.com/s/agxyxntrbwko7t1/participants_data.zip?dl=0'\n",
        "os.environ['download_link'] = dropbox_link  \n",
        "!echo $download_link \n",
        "!wget -O participants_data.zip -c $download_link\n",
        "!wget -c https://raw.githubusercontent.com/Neural-Dynamics-of-Visual-Cognition-FUB/Algonauts2021_devkit/main/class_names_ImageNet.txt"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://www.dropbox.com/s/agxyxntrbwko7t1/participants_data.zip?dl=0\n",
            "--2021-08-13 14:35:20--  https://www.dropbox.com/s/agxyxntrbwko7t1/participants_data.zip?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.81.18, 2620:100:6031:18::a27d:5112\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.81.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/agxyxntrbwko7t1/participants_data.zip [following]\n",
            "--2021-08-13 14:35:21--  https://www.dropbox.com/s/raw/agxyxntrbwko7t1/participants_data.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com/cd/0/inline/BULBjugWmwIxp0yiFgpMLllk-xUPabS-64LjejmVgLZhxHresjShMKuYbh1vpwMNtshKqQfVTB5MVt5cEqVHizOIFqmU-sntiNX00boZL1rFR7SUYPlJQzoy6lzwnaJoDIOakGAbOuIJPOg-TburFfUW/file# [following]\n",
            "--2021-08-13 14:35:21--  https://uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com/cd/0/inline/BULBjugWmwIxp0yiFgpMLllk-xUPabS-64LjejmVgLZhxHresjShMKuYbh1vpwMNtshKqQfVTB5MVt5cEqVHizOIFqmU-sntiNX00boZL1rFR7SUYPlJQzoy6lzwnaJoDIOakGAbOuIJPOg-TburFfUW/file\n",
            "Resolving uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com (uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f\n",
            "Connecting to uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com (uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/BUJLjQ5mBhx9srn8NQcAKdUjShv5j3VQZds-8NLuuDxxFyl1-SxzRc8W4hYAf7d2wCEg4ytQ_UJPgIJ2N8YETzTW_sC8Bp0-uFmva-GTupZCmUGieWRlmSSL99icZtVwLUS69Tdh5lAL6p4kX2ARxGJNtySLrsm7ZJZk9WrHpbT5SJxxxVDuFt2CZ10ye4lbgLtogarUb37eD9XJYcHchRHxsZTDIfzk9IXRSjhhpA1elzUztWgfIi-PeNjBqEo5XrbfuH2GFtCHCDF6BJibgG0KA5d1S4ONjpy0g-DdJtK3dtqFsEoYH35xfvuhswoAl7fSb-Gt7cg5am8xSoTAe_ptvXHasB_fM667kDzIB5Omy_gT7vw05357Ck5aQUMPUk4/file [following]\n",
            "--2021-08-13 14:35:22--  https://uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com/cd/0/inline2/BUJLjQ5mBhx9srn8NQcAKdUjShv5j3VQZds-8NLuuDxxFyl1-SxzRc8W4hYAf7d2wCEg4ytQ_UJPgIJ2N8YETzTW_sC8Bp0-uFmva-GTupZCmUGieWRlmSSL99icZtVwLUS69Tdh5lAL6p4kX2ARxGJNtySLrsm7ZJZk9WrHpbT5SJxxxVDuFt2CZ10ye4lbgLtogarUb37eD9XJYcHchRHxsZTDIfzk9IXRSjhhpA1elzUztWgfIi-PeNjBqEo5XrbfuH2GFtCHCDF6BJibgG0KA5d1S4ONjpy0g-DdJtK3dtqFsEoYH35xfvuhswoAl7fSb-Gt7cg5am8xSoTAe_ptvXHasB_fM667kDzIB5Omy_gT7vw05357Ck5aQUMPUk4/file\n",
            "Reusing existing connection to uce3a4c7a44dac16491c07785f45.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "\n",
            "    The file is already fully retrieved; nothing to do.\n",
            "\n",
            "--2021-08-13 14:35:22--  https://raw.githubusercontent.com/Neural-Dynamics-of-Visual-Cognition-FUB/Algonauts2021_devkit/main/class_names_ImageNet.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 416 Range Not Satisfiable\n",
            "\n",
            "    The file is already fully retrieved; nothing to do.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bc-xzICNCh8w"
      },
      "source": [
        "# unzip data file \n",
        "%%capture\n",
        "!unzip -o participants_data.zip"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuCTlFUHxKC9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf6b7bd1-0213-4f41-88ef-3ed768a30b0e"
      },
      "source": [
        "# download pretrained model\n",
        "savedir=\"model_data_keras2\"\n",
        "!mkdir -p -- \"$savedir\"\n",
        "!wget https://www.dropbox.com/s/iutxm0anhxqca0z/model_data_keras2.zip?dl=0 -O $savedir/model_data_keras2.zip\n",
        "!unzip $savedir/model_data_keras2.zip -d $savedir\n",
        "!rm $savedir/model_data_keras2.zip\n",
        "!mv $savedir/model_data_keras2/* $savedir\n",
        "!rm -r $savedir/model_data_keras2"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-13 14:37:33--  https://www.dropbox.com/s/iutxm0anhxqca0z/model_data_keras2.zip?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.81.18, 2620:100:6031:18::a27d:5112\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.81.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/iutxm0anhxqca0z/model_data_keras2.zip [following]\n",
            "--2021-08-13 14:37:33--  https://www.dropbox.com/s/raw/iutxm0anhxqca0z/model_data_keras2.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com/cd/0/inline/BUIwnQAVJ5QWdKFx_CIky-f-UsE2clRGpLMLSQvMiZ9FS7i4dHo9mCYKxkkJTD6QLGcnoV6pl7yulZN3EVdNN6DrBNL-evUU3_8yWJsQ2BZIy7kmRwECjpHLAec7GirbVjFdjAqCdJuRoMUSKb0lO-hU/file# [following]\n",
            "--2021-08-13 14:37:34--  https://uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com/cd/0/inline/BUIwnQAVJ5QWdKFx_CIky-f-UsE2clRGpLMLSQvMiZ9FS7i4dHo9mCYKxkkJTD6QLGcnoV6pl7yulZN3EVdNN6DrBNL-evUU3_8yWJsQ2BZIy7kmRwECjpHLAec7GirbVjFdjAqCdJuRoMUSKb0lO-hU/file\n",
            "Resolving uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com (uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f\n",
            "Connecting to uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com (uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/BUJ9SsnWRia6EXib1pF_JHuX6I5u8IwxoZKHijAEMiaWoa6cigTU_EyPVqypanzmAzoi2CHJ-hRS4AdHWt5tNOHZFQuo3407Zx5ZervAVquv_Gth5F89ABQCAAyHtIifd369H376uh3zSzv0tijLdtstizIkGT414uLYlYhazsWrSFOe5p-QkbgDJ6YCeo2RipQShpAM53Azdu0neJXI9jKvlQVlsKCzMcTNeGq786wVgPFidP2vcReQ9hJUlP4ZhYwjkRjGquAIwxZWch5lSX9qZcebLCJ0PdIdA4_vrh29STeiY2ihE4X992fhYoTQo4P-fySaqKWNTG0rtK-vfDXUGK7IpeMsjhmliD5sYg_P6IZeSWKArYgymte0vdCd0Is/file [following]\n",
            "--2021-08-13 14:37:34--  https://uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com/cd/0/inline2/BUJ9SsnWRia6EXib1pF_JHuX6I5u8IwxoZKHijAEMiaWoa6cigTU_EyPVqypanzmAzoi2CHJ-hRS4AdHWt5tNOHZFQuo3407Zx5ZervAVquv_Gth5F89ABQCAAyHtIifd369H376uh3zSzv0tijLdtstizIkGT414uLYlYhazsWrSFOe5p-QkbgDJ6YCeo2RipQShpAM53Azdu0neJXI9jKvlQVlsKCzMcTNeGq786wVgPFidP2vcReQ9hJUlP4ZhYwjkRjGquAIwxZWch5lSX9qZcebLCJ0PdIdA4_vrh29STeiY2ihE4X992fhYoTQo4P-fySaqKWNTG0rtK-vfDXUGK7IpeMsjhmliD5sYg_P6IZeSWKArYgymte0vdCd0Is/file\n",
            "Reusing existing connection to uc0df64945142a0e5fa3ac4bbc31.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 154392433 (147M) [application/zip]\n",
            "Saving to: ‘model_data_keras2/model_data_keras2.zip’\n",
            "\n",
            "model_data_keras2/m 100%[===================>] 147.24M  16.5MB/s    in 9.4s    \n",
            "\n",
            "2021-08-13 14:37:44 (15.7 MB/s) - ‘model_data_keras2/model_data_keras2.zip’ saved [154392433/154392433]\n",
            "\n",
            "Archive:  model_data_keras2/model_data_keras2.zip\n",
            "  inflating: model_data_keras2/model_data_keras2/prednet_kitti_model-extrapfinetuned.json  \n",
            "  inflating: model_data_keras2/model_data_keras2/prednet_kitti_model.json  \n",
            "   creating: model_data_keras2/model_data_keras2/tensorflow_weights/\n",
            "  inflating: model_data_keras2/model_data_keras2/tensorflow_weights/prednet_kitti_weights.hdf5  \n",
            "  inflating: model_data_keras2/model_data_keras2/tensorflow_weights/prednet_kitti_weights-Lall.hdf5  \n",
            "  inflating: model_data_keras2/model_data_keras2/tensorflow_weights/prednet_kitti_weights-extrapfinetuned.hdf5  \n",
            "   creating: model_data_keras2/model_data_keras2/theano_weights/\n",
            "  inflating: model_data_keras2/model_data_keras2/theano_weights/prednet_kitti_weights-extrapfinetuned.hdf5  \n",
            "  inflating: model_data_keras2/model_data_keras2/theano_weights/prednet_kitti_weights.hdf5  \n",
            "  inflating: model_data_keras2/model_data_keras2/theano_weights/prednet_kitti_weights-Lall.hdf5  \n",
            "mv: cannot move 'model_data_keras2/model_data_keras2/tensorflow_weights' to 'model_data_keras2/tensorflow_weights': Directory not empty\n",
            "mv: cannot move 'model_data_keras2/model_data_keras2/theano_weights' to 'model_data_keras2/theano_weights': Directory not empty\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rzGLI22CkfB"
      },
      "source": [
        "# **Video preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaB2fqzIClnr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58903ee3-8aed-4cac-fa11-0a9170791e90"
      },
      "source": [
        "# Colab directory containing videos\n",
        "video_dir = '/content/AlgonautsVideos268_All_30fpsmax'\n",
        "\n",
        "# get path of all videos\n",
        "video_list = glob.glob(video_dir + '/*.mp4')\n",
        "\n",
        "# sort videos in ascending order\n",
        "video_list.sort()\n",
        "\n",
        "print('Total number of videos: ', len(video_list))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of videos:  1102\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCTF6gkSCtTt"
      },
      "source": [
        "def sample_video_from_mp4(file, num_frames = 16):\n",
        "    \"\"\"This function takes a mp4 video file as input and returns\n",
        "    an array of uniformly sampled frames.\n",
        "    \n",
        "    Args\n",
        "    ----------\n",
        "    file : str\n",
        "        path to mp4 video file\n",
        "    num_frames : int\n",
        "        number of frames to select with uniform frame sampling\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    frames: list of frames as PIL images\n",
        "    num_frames: number of sampled frames\n",
        "\n",
        "    \"\"\"\n",
        "    \n",
        "    # read video file\n",
        "    video = VideoReader(file)\n",
        "\n",
        "    # get total number of video frames\n",
        "    total_frames = len(video)\n",
        "\n",
        "    # create frame indices \n",
        "    frame_indices = np.linspace(0, total_frames-1, num_frames, dtype = np.int) \n",
        "\n",
        "    video_frames = []\n",
        "\n",
        "    # fill list of video frames as PIL images\n",
        "    for i in frame_indices:\n",
        "      video_frames.append(Image.fromarray(video[i].asnumpy()))\n",
        "\n",
        "    # convert to numpy array\n",
        "    video_frames = np.array([np.asarray(f) for f in video_frames])\n",
        "\n",
        "    return video_frames, num_frames\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HkZ9Ll0fCwgb"
      },
      "source": [
        "# **Neural network feature extraction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0U08ONGCzkA"
      },
      "source": [
        "## Pretrained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhpP7S5mDBK-"
      },
      "source": [
        "#@title Helper function\n",
        "\n",
        "legacy_prednet_support = generate_legacy_interface(\n",
        "    allowed_positional_args=['stack_sizes', 'R_stack_sizes',\n",
        "                            'A_filt_sizes', 'Ahat_filt_sizes', 'R_filt_sizes'],\n",
        "    conversions=[('dim_ordering', 'data_format'),\n",
        "                 ('consume_less', 'implementation')],\n",
        "    value_conversions={'dim_ordering': {'tf': 'channels_last',\n",
        "                                        'th': 'channels_first',\n",
        "                                        'default': None},\n",
        "                        'consume_less': {'cpu': 0,\n",
        "                                        'mem': 1,\n",
        "                                        'gpu': 2}},\n",
        "    preprocessor=recurrent_args_preprocessor)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cBRccArxCMT"
      },
      "source": [
        "#@title PredNet architecture\n",
        "\n",
        "# Reference: https://arxiv.org/abs/1605.08104, github repo: https://github.com/coxlab/prednet\n",
        "\n",
        "\n",
        "class PredNet(Recurrent):\n",
        "    '''PredNet architecture - Lotter 2016.\n",
        "        Stacked convolutional LSTM inspired by predictive coding principles.\n",
        "\n",
        "    # Arguments\n",
        "        stack_sizes: number of channels in targets (A) and predictions (Ahat) in each layer of the architecture.\n",
        "            Length is the number of layers in the architecture.\n",
        "            First element is the number of channels in the input.\n",
        "            Ex. (3, 16, 32) would correspond to a 3 layer architecture that takes in RGB images and has 16 and 32\n",
        "                channels in the second and third layers, respectively.\n",
        "        R_stack_sizes: number of channels in the representation (R) modules.\n",
        "            Length must equal length of stack_sizes, but the number of channels per layer can be different.\n",
        "        A_filt_sizes: filter sizes for the target (A) modules.\n",
        "            Has length of 1 - len(stack_sizes).\n",
        "            Ex. (3, 3) would mean that targets for layers 2 and 3 are computed by a 3x3 convolution of the errors (E)\n",
        "                from the layer below (followed by max-pooling)\n",
        "        Ahat_filt_sizes: filter sizes for the prediction (Ahat) modules.\n",
        "            Has length equal to length of stack_sizes.\n",
        "            Ex. (3, 3, 3) would mean that the predictions for each layer are computed by a 3x3 convolution of the\n",
        "                representation (R) modules at each layer.\n",
        "        R_filt_sizes: filter sizes for the representation (R) modules.\n",
        "            Has length equal to length of stack_sizes.\n",
        "            Corresponds to the filter sizes for all convolutions in the LSTM.\n",
        "        pixel_max: the maximum pixel value.\n",
        "            Used to clip the pixel-layer prediction.\n",
        "        error_activation: activation function for the error (E) units.\n",
        "        A_activation: activation function for the target (A) and prediction (A_hat) units.\n",
        "        LSTM_activation: activation function for the cell and hidden states of the LSTM.\n",
        "        LSTM_inner_activation: activation function for the gates in the LSTM.\n",
        "        output_mode: either 'error', 'prediction', 'all' or layer specification (ex. R2, see below).\n",
        "            Controls what is outputted by the PredNet.\n",
        "            If 'error', the mean response of the error (E) units of each layer will be outputted.\n",
        "                That is, the output shape will be (batch_size, nb_layers).\n",
        "            If 'prediction', the frame prediction will be outputted.\n",
        "            If 'all', the output will be the frame prediction concatenated with the mean layer errors.\n",
        "                The frame prediction is flattened before concatenation.\n",
        "                Nomenclature of 'all' is kept for backwards compatibility, but should not be confused with returning all of the layers of the model\n",
        "            For returning the features of a particular layer, output_mode should be of the form unit_type + layer_number.\n",
        "                For instance, to return the features of the LSTM \"representational\" units in the lowest layer, output_mode should be specificied as 'R0'.\n",
        "                The possible unit types are 'R', 'Ahat', 'A', and 'E' corresponding to the 'representation', 'prediction', 'target', and 'error' units respectively.\n",
        "        extrap_start_time: time step for which model will start extrapolating.\n",
        "            Starting at this time step, the prediction from the previous time step will be treated as the \"actual\"\n",
        "        data_format: 'channels_first' or 'channels_last'.\n",
        "            It defaults to the `image_data_format` value found in your\n",
        "            Keras config file at `~/.keras/keras.json`.\n",
        "\n",
        "    # References\n",
        "        - [Deep predictive coding networks for video prediction and unsupervised learning](https://arxiv.org/abs/1605.08104)\n",
        "        - [Long short-term memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf)\n",
        "        - [Convolutional LSTM network: a machine learning approach for precipitation nowcasting](http://arxiv.org/abs/1506.04214)\n",
        "        - [Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects](http://www.nature.com/neuro/journal/v2/n1/pdf/nn0199_79.pdf)\n",
        "    '''\n",
        "    @legacy_prednet_support\n",
        "    def __init__(self, stack_sizes, R_stack_sizes,\n",
        "                 A_filt_sizes, Ahat_filt_sizes, R_filt_sizes,\n",
        "                 pixel_max=1., error_activation='relu', A_activation='relu',\n",
        "                 LSTM_activation='tanh', LSTM_inner_activation='hard_sigmoid',\n",
        "                 output_mode='error', extrap_start_time=None,\n",
        "                 data_format=K.image_data_format(), **kwargs):\n",
        "        self.stack_sizes = stack_sizes\n",
        "        self.nb_layers = len(stack_sizes)\n",
        "        assert len(R_stack_sizes) == self.nb_layers, 'len(R_stack_sizes) must equal len(stack_sizes)'\n",
        "        self.R_stack_sizes = R_stack_sizes\n",
        "        assert len(A_filt_sizes) == (self.nb_layers - 1), 'len(A_filt_sizes) must equal len(stack_sizes) - 1'\n",
        "        self.A_filt_sizes = A_filt_sizes\n",
        "        assert len(Ahat_filt_sizes) == self.nb_layers, 'len(Ahat_filt_sizes) must equal len(stack_sizes)'\n",
        "        self.Ahat_filt_sizes = Ahat_filt_sizes\n",
        "        assert len(R_filt_sizes) == (self.nb_layers), 'len(R_filt_sizes) must equal len(stack_sizes)'\n",
        "        self.R_filt_sizes = R_filt_sizes\n",
        "\n",
        "        self.pixel_max = pixel_max\n",
        "        self.error_activation = activations.get(error_activation)\n",
        "        self.A_activation = activations.get(A_activation)\n",
        "        self.LSTM_activation = activations.get(LSTM_activation)\n",
        "        self.LSTM_inner_activation = activations.get(LSTM_inner_activation)\n",
        "\n",
        "        default_output_modes = ['prediction', 'error', 'all']\n",
        "        layer_output_modes = [layer + str(n) for n in range(self.nb_layers) for layer in ['R', 'E', 'A', 'Ahat']]\n",
        "        assert output_mode in default_output_modes + layer_output_modes, 'Invalid output_mode: ' + str(output_mode)\n",
        "        self.output_mode = output_mode\n",
        "        if self.output_mode in layer_output_modes:\n",
        "            self.output_layer_type = self.output_mode[:-1]\n",
        "            self.output_layer_num = int(self.output_mode[-1])\n",
        "        else:\n",
        "            self.output_layer_type = None\n",
        "            self.output_layer_num = None\n",
        "        self.extrap_start_time = extrap_start_time\n",
        "\n",
        "        assert data_format in {'channels_last', 'channels_first'}, 'data_format must be in {channels_last, channels_first}'\n",
        "        self.data_format = data_format\n",
        "        self.channel_axis = -3 if data_format == 'channels_first' else -1\n",
        "        self.row_axis = -2 if data_format == 'channels_first' else -3\n",
        "        self.column_axis = -1 if data_format == 'channels_first' else -2\n",
        "        super(PredNet, self).__init__(**kwargs)\n",
        "        self.input_spec = [InputSpec(ndim=5)]\n",
        "\n",
        "    def compute_output_shape(self, input_shape):\n",
        "        if self.output_mode == 'prediction':\n",
        "            out_shape = input_shape[2:]\n",
        "        elif self.output_mode == 'error':\n",
        "            out_shape = (self.nb_layers,)\n",
        "        elif self.output_mode == 'all':\n",
        "            out_shape = (np.prod(input_shape[2:]) + self.nb_layers,)\n",
        "        else:\n",
        "            stack_str = 'R_stack_sizes' if self.output_layer_type == 'R' else 'stack_sizes'\n",
        "            stack_mult = 2 if self.output_layer_type == 'E' else 1\n",
        "            out_stack_size = stack_mult * getattr(self, stack_str)[self.output_layer_num]\n",
        "            out_nb_row = input_shape[self.row_axis] / 2**self.output_layer_num\n",
        "            out_nb_col = input_shape[self.column_axis] / 2**self.output_layer_num\n",
        "            if self.data_format == 'channels_first':\n",
        "                out_shape = (out_stack_size, out_nb_row, out_nb_col)\n",
        "            else:\n",
        "                out_shape = (out_nb_row, out_nb_col, out_stack_size)\n",
        "\n",
        "        if self.return_sequences:\n",
        "            return (input_shape[0], input_shape[1]) + out_shape\n",
        "        else:\n",
        "            return (input_shape[0],) + out_shape\n",
        "\n",
        "    def get_initial_state(self, x):\n",
        "        input_shape = self.input_spec[0].shape\n",
        "        init_nb_row = input_shape[self.row_axis]\n",
        "        init_nb_col = input_shape[self.column_axis]\n",
        "\n",
        "        base_initial_state = K.zeros_like(x)  # (samples, timesteps) + image_shape\n",
        "        non_channel_axis = -1 if self.data_format == 'channels_first' else -2\n",
        "        for _ in range(2):\n",
        "            base_initial_state = K.sum(base_initial_state, axis=non_channel_axis)\n",
        "        base_initial_state = K.sum(base_initial_state, axis=1)  # (samples, nb_channels)\n",
        "\n",
        "        initial_states = []\n",
        "        states_to_pass = ['r', 'c', 'e']\n",
        "        nlayers_to_pass = {u: self.nb_layers for u in states_to_pass}\n",
        "        if self.extrap_start_time is not None:\n",
        "           states_to_pass.append('ahat')  # pass prediction in states so can use as actual for t+1 when extrapolating\n",
        "           nlayers_to_pass['ahat'] = 1\n",
        "        for u in states_to_pass:\n",
        "            for l in range(nlayers_to_pass[u]):\n",
        "                ds_factor = 2 ** l\n",
        "                nb_row = init_nb_row // ds_factor\n",
        "                nb_col = init_nb_col // ds_factor\n",
        "                if u in ['r', 'c']:\n",
        "                    stack_size = self.R_stack_sizes[l]\n",
        "                elif u == 'e':\n",
        "                    stack_size = 2 * self.stack_sizes[l]\n",
        "                elif u == 'ahat':\n",
        "                    stack_size = self.stack_sizes[l]\n",
        "                output_size = stack_size * nb_row * nb_col  # flattened size\n",
        "\n",
        "                reducer = K.zeros((input_shape[self.channel_axis], output_size)) # (nb_channels, output_size)\n",
        "                initial_state = K.dot(base_initial_state, reducer) # (samples, output_size)\n",
        "                if self.data_format == 'channels_first':\n",
        "                    output_shp = (-1, stack_size, nb_row, nb_col)\n",
        "                else:\n",
        "                    output_shp = (-1, nb_row, nb_col, stack_size)\n",
        "                initial_state = K.reshape(initial_state, output_shp)\n",
        "                initial_states += [initial_state]\n",
        "\n",
        "        if keras.backend.backend() == 'theano':\n",
        "            from theano import tensor as T\n",
        "            # There is a known issue in the Theano scan op when dealing with inputs whose shape is 1 along a dimension.\n",
        "            # In our case, this is a problem when training on grayscale images, and the below line fixes it.\n",
        "            initial_states = [T.unbroadcast(init_state, 0, 1) for init_state in initial_states]\n",
        "\n",
        "        if self.extrap_start_time is not None:\n",
        "            initial_states += [K.variable(0, int if K.backend() != 'tensorflow' else 'int32')]  # the last state will correspond to the current timestep\n",
        "        return initial_states\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        self.input_spec = [InputSpec(shape=input_shape)]\n",
        "        self.conv_layers = {c: [] for c in ['i', 'f', 'c', 'o', 'a', 'ahat']}\n",
        "\n",
        "        for l in range(self.nb_layers):\n",
        "            for c in ['i', 'f', 'c', 'o']:\n",
        "                act = self.LSTM_activation if c == 'c' else self.LSTM_inner_activation\n",
        "                self.conv_layers[c].append(Conv2D(self.R_stack_sizes[l], self.R_filt_sizes[l], padding='same', activation=act, data_format=self.data_format))\n",
        "\n",
        "            act = 'relu' if l == 0 else self.A_activation\n",
        "            self.conv_layers['ahat'].append(Conv2D(self.stack_sizes[l], self.Ahat_filt_sizes[l], padding='same', activation=act, data_format=self.data_format))\n",
        "\n",
        "            if l < self.nb_layers - 1:\n",
        "                self.conv_layers['a'].append(Conv2D(self.stack_sizes[l+1], self.A_filt_sizes[l], padding='same', activation=self.A_activation, data_format=self.data_format))\n",
        "\n",
        "        self.upsample = UpSampling2D(data_format=self.data_format)\n",
        "        self.pool = MaxPooling2D(data_format=self.data_format)\n",
        "\n",
        "        self.trainable_weights = []\n",
        "        nb_row, nb_col = (input_shape[-2], input_shape[-1]) if self.data_format == 'channels_first' else (input_shape[-3], input_shape[-2])\n",
        "        for c in sorted(self.conv_layers.keys()):\n",
        "            for l in range(len(self.conv_layers[c])):\n",
        "                ds_factor = 2 ** l\n",
        "                if c == 'ahat':\n",
        "                    nb_channels = self.R_stack_sizes[l]\n",
        "                elif c == 'a':\n",
        "                    nb_channels = 2 * self.stack_sizes[l]\n",
        "                else:\n",
        "                    nb_channels = self.stack_sizes[l] * 2 + self.R_stack_sizes[l]\n",
        "                    if l < self.nb_layers - 1:\n",
        "                        nb_channels += self.R_stack_sizes[l+1]\n",
        "                in_shape = (input_shape[0], nb_channels, nb_row // ds_factor, nb_col // ds_factor)\n",
        "                if self.data_format == 'channels_last': in_shape = (in_shape[0], in_shape[2], in_shape[3], in_shape[1])\n",
        "                with K.name_scope('layer_' + c + '_' + str(l)):\n",
        "                    self.conv_layers[c][l].build(in_shape)\n",
        "                self.trainable_weights += self.conv_layers[c][l].trainable_weights\n",
        "\n",
        "        self.states = [None] * self.nb_layers*3\n",
        "\n",
        "        if self.extrap_start_time is not None:\n",
        "            self.t_extrap = K.variable(self.extrap_start_time, int if K.backend() != 'tensorflow' else 'int32')\n",
        "            self.states += [None] * 2  # [previous frame prediction, timestep]\n",
        "\n",
        "    def step(self, a, states):\n",
        "        r_tm1 = states[:self.nb_layers]\n",
        "        c_tm1 = states[self.nb_layers:2*self.nb_layers]\n",
        "        e_tm1 = states[2*self.nb_layers:3*self.nb_layers]\n",
        "\n",
        "        if self.extrap_start_time is not None:\n",
        "            t = states[-1]\n",
        "            a = K.switch(t >= self.t_extrap, states[-2], a)  # if past self.extrap_start_time, the previous prediction will be treated as the actual\n",
        "\n",
        "        c = []\n",
        "        r = []\n",
        "        e = []\n",
        "\n",
        "        # Update R units starting from the top\n",
        "        for l in reversed(range(self.nb_layers)):\n",
        "            inputs = [r_tm1[l], e_tm1[l]]\n",
        "            if l < self.nb_layers - 1:\n",
        "                inputs.append(r_up)\n",
        "\n",
        "            inputs = K.concatenate(inputs, axis=self.channel_axis)\n",
        "            i = self.conv_layers['i'][l].call(inputs)\n",
        "            f = self.conv_layers['f'][l].call(inputs)\n",
        "            o = self.conv_layers['o'][l].call(inputs)\n",
        "            _c = f * c_tm1[l] + i * self.conv_layers['c'][l].call(inputs)\n",
        "            _r = o * self.LSTM_activation(_c)\n",
        "            c.insert(0, _c)\n",
        "            r.insert(0, _r)\n",
        "\n",
        "            if l > 0:\n",
        "                r_up = self.upsample.call(_r)\n",
        "\n",
        "        # Update feedforward path starting from the bottom\n",
        "        for l in range(self.nb_layers):\n",
        "            ahat = self.conv_layers['ahat'][l].call(r[l])\n",
        "            if l == 0:\n",
        "                ahat = K.minimum(ahat, self.pixel_max)\n",
        "                frame_prediction = ahat\n",
        "\n",
        "            # compute errors\n",
        "            e_up = self.error_activation(ahat - a)\n",
        "            e_down = self.error_activation(a - ahat)\n",
        "\n",
        "            e.append(K.concatenate((e_up, e_down), axis=self.channel_axis))\n",
        "\n",
        "            if self.output_layer_num == l:\n",
        "                if self.output_layer_type == 'A':\n",
        "                    output = a\n",
        "                elif self.output_layer_type == 'Ahat':\n",
        "                    output = ahat\n",
        "                elif self.output_layer_type == 'R':\n",
        "                    output = r[l]\n",
        "                elif self.output_layer_type == 'E':\n",
        "                    output = e[l]\n",
        "\n",
        "            if l < self.nb_layers - 1:\n",
        "                a = self.conv_layers['a'][l].call(e[l])\n",
        "                a = self.pool.call(a)  # target for next layer\n",
        "\n",
        "        if self.output_layer_type is None:\n",
        "            if self.output_mode == 'prediction':\n",
        "                output = frame_prediction\n",
        "            else:\n",
        "                for l in range(self.nb_layers):\n",
        "                    layer_error = K.mean(K.batch_flatten(e[l]), axis=-1, keepdims=True)\n",
        "                    all_error = layer_error if l == 0 else K.concatenate((all_error, layer_error), axis=-1)\n",
        "                if self.output_mode == 'error':\n",
        "                    output = all_error\n",
        "                else:\n",
        "                    output = K.concatenate((K.batch_flatten(frame_prediction), all_error), axis=-1)\n",
        "\n",
        "        states = r + c + e\n",
        "        if self.extrap_start_time is not None:\n",
        "            states += [frame_prediction, t + 1]\n",
        "        return output, states\n",
        "\n",
        "    def get_config(self):\n",
        "        config = {'stack_sizes': self.stack_sizes,\n",
        "                  'R_stack_sizes': self.R_stack_sizes,\n",
        "                  'A_filt_sizes': self.A_filt_sizes,\n",
        "                  'Ahat_filt_sizes': self.Ahat_filt_sizes,\n",
        "                  'R_filt_sizes': self.R_filt_sizes,\n",
        "                  'pixel_max': self.pixel_max,\n",
        "                  'error_activation': self.error_activation.__name__,\n",
        "                  'A_activation': self.A_activation.__name__,\n",
        "                  'LSTM_activation': self.LSTM_activation.__name__,\n",
        "                  'LSTM_inner_activation': self.LSTM_inner_activation.__name__,\n",
        "                  'data_format': self.data_format,\n",
        "                  'extrap_start_time': self.extrap_start_time,\n",
        "                  'output_mode': self.output_mode}\n",
        "        base_config = super(PredNet, self).get_config()\n",
        "        return dict(list(base_config.items()) + list(config.items()))\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-YxsLNKxM8N"
      },
      "source": [
        "# pretrained weights directory\n",
        "WEIGHTS_DIR = './model_data_keras2/'\n",
        "\n",
        "# weights and model files\n",
        "weights_file = os.path.join(WEIGHTS_DIR, 'tensorflow_weights/prednet_kitti_weights.hdf5')\n",
        "json_file = os.path.join(WEIGHTS_DIR, 'prednet_kitti_model.json')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Udi-xgPtxOgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efb0aeee-4373-481f-c1ef-ff67cf29b3cf"
      },
      "source": [
        "# load model weights\n",
        "with open(json_file, 'r') as f:\n",
        "    json_string = f.read()\n",
        "    print(json_string)\n",
        "\n",
        "train_model = model_from_json(json_string, custom_objects = {'PredNet': PredNet})\n",
        "train_model.load_weights(weights_file)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\"class_name\": \"Model\", \"keras_version\": \"2.0.6\", \"config\": {\"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"dtype\": \"float32\", \"batch_input_shape\": [null, 10, 3, 128, 160], \"name\": \"input_1\", \"sparse\": false}, \"inbound_nodes\": [], \"name\": \"input_1\"}, {\"class_name\": \"PredNet\", \"config\": {\"dtype\": \"float32\", \"trainable\": true, \"pixel_max\": 1.0, \"Ahat_filt_sizes\": [3, 3, 3, 3], \"return_state\": false, \"R_filt_sizes\": [3, 3, 3, 3], \"unroll\": false, \"batch_input_shape\": [null, null, null], \"LSTM_inner_activation\": \"hard_sigmoid\", \"output_mode\": \"error\", \"stateful\": false, \"error_activation\": \"relu\", \"A_activation\": \"relu\", \"A_filt_sizes\": [3, 3, 3], \"stack_sizes\": [3, 48, 96, 192], \"name\": \"prednet_1\", \"R_stack_sizes\": [3, 48, 96, 192], \"data_format\": \"channels_first\", \"implementation\": 0, \"extrap_start_time\": null, \"go_backwards\": false, \"return_sequences\": true, \"LSTM_activation\": \"tanh\"}, \"inbound_nodes\": [[[\"input_1\", 0, 0, {}]]], \"name\": \"prednet_1\"}, {\"class_name\": \"TimeDistributed\", \"config\": {\"layer\": {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": false, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 1, \"batch_input_shape\": [null, null], \"use_bias\": true, \"activity_regularizer\": null}}, \"trainable\": false, \"name\": \"timedistributed_1\"}, \"inbound_nodes\": [[[\"prednet_1\", 0, 0, {}]]], \"name\": \"timedistributed_1\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_1\"}, \"inbound_nodes\": [[[\"timedistributed_1\", 0, 0, {}]]], \"name\": \"flatten_1\"}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": false, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 1, \"batch_input_shape\": [null, null], \"use_bias\": true, \"activity_regularizer\": null}, \"inbound_nodes\": [[[\"flatten_1\", 0, 0, {}]]], \"name\": \"dense_2\"}], \"input_layers\": [[\"input_1\", 0, 0]], \"output_layers\": [[\"dense_2\", 0, 0]], \"name\": \"model_1\"}, \"backend\": \"theano\"}\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnZSOkCcxQ3G"
      },
      "source": [
        "# set parameters\n",
        "batch_size = 1\n",
        "nt = 16   # time steps/frames"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRkXhLZvxShY"
      },
      "source": [
        "# model configuration\n",
        "layer_config = train_model.layers[1].get_config()\n",
        "layer_config['output_mode'] = 'error'      ########## change output mode ###########\n",
        "data_format = layer_config['data_format'] if 'data_format' in layer_config else layer_config['dim_ordering']\n",
        "test_prednet = PredNet(weights=train_model.layers[1].get_weights(), **layer_config)\n",
        "input_shape = list(train_model.layers[0].batch_input_shape[1:])\n",
        "input_shape[0] = nt\n",
        "inputs = keras.Input(shape=tuple(input_shape))\n",
        "predictions = test_prednet(inputs)\n",
        "test_model = Model(inputs=inputs, outputs=predictions)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7rMFdPD2Sm1",
        "outputId": "f6299a51-7d52-40b8-98f6-e543216bdcfd"
      },
      "source": [
        "train_model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 10, 3, 128, 160)   0         \n",
            "_________________________________________________________________\n",
            "prednet_1 (PredNet)          (None, 10, 4)             6915948   \n",
            "_________________________________________________________________\n",
            "timedistributed_1 (TimeDistr (None, 10, 1)             5         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 6,915,964\n",
            "Trainable params: 6,915,948\n",
            "Non-trainable params: 16\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sm6R5csj2Wtx",
        "outputId": "ec47e7a8-bfc8-438a-fbe4-15e706700f15"
      },
      "source": [
        "test_model.summary()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 16, 3, 128, 160)   0         \n",
            "_________________________________________________________________\n",
            "prednet_1 (PredNet)          (None, 16, 4)             6915948   \n",
            "=================================================================\n",
            "Total params: 6,915,948\n",
            "Trainable params: 6,915,948\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5BoRJuIDwS1f",
        "outputId": "308fee32-7d95-4a0e-f1ee-ffc8faa4a5c7"
      },
      "source": [
        "test_model"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.training.Model at 0x7f86325f7e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAAFAxHAxfRA"
      },
      "source": [
        "### Example prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTytCEr8xhgB"
      },
      "source": [
        "example_frames, num_frames = sample_video_from_mp4(video_list[550])\n",
        "\n",
        "# preprocess example frames\n",
        "example_frames = example_frames/255\n",
        "example_frames = tf.image.resize(example_frames, (128, 160))\n",
        "example_frames = tf.expand_dims(example_frames, axis = 0)\n",
        "example_frames = tf.transpose(example_frames, (0, 1, 4, 2, 3))\n",
        "example_frames = example_frames.eval(session=tf.compat.v1.Session())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xx8R7WLixjWn"
      },
      "source": [
        "# predict\n",
        "X_hat = test_model.predict(example_frames, batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4OvI-WXYoEG",
        "outputId": "2471fc40-2ea1-4380-91f8-2912e38c9a03"
      },
      "source": [
        "example_frames = np.transpose(example_frames, axes=(0,1,3,4,2))\n",
        "example_frames.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 16, 128, 160, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plWiL7rTYYxq",
        "outputId": "3e1a45e1-69fa-4ebe-f608-380e2819332d"
      },
      "source": [
        "X_hat = np.transpose(example_frames, axes=(0,1,2,3,4))\n",
        "X_hat.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 16, 128, 160, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "9K3P4y3fYa4P",
        "outputId": "d124fa02-ffeb-4bd0-d87e-d43fa4a7f30a"
      },
      "source": [
        "plt.imshow(example_frames[0,14])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa9fea5c990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "YG7pqh4rZBJI",
        "outputId": "8b45d820-3e76-43f4-e541-e7549ed58b50"
      },
      "source": [
        "plt.imshow(X_hat[0,14])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa9fea5c810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_ivWo4xDCPq"
      },
      "source": [
        "## Model activations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJfZPNnz2wlF"
      },
      "source": [
        "video_frames, num_frames = sample_video_from_mp4(video_list[1])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JYH_4ZMe22Gt",
        "outputId": "5598812c-2fbb-49aa-bf3d-bba991c00358"
      },
      "source": [
        "print(video_frames.shape)\n",
        "print(num_frames)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(16, 268, 268, 3)\n",
            "16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7z4XUaJ1z2_",
        "outputId": "b3aa0a4b-ceae-43ef-905f-848c028645a3"
      },
      "source": [
        "#fake videoframe with np\n",
        "#model = test_model\n",
        "#x1 = test_model.predict(video_frames, batch_size=1)\n",
        "# inspect x1 and its size\n",
        "\n",
        "video_frames = video_frames / 255.0\n",
        "video_frames = tf.image.resize(video_frames, (128, 160))\n",
        "video_frames = tf.expand_dims(video_frames, axis = 0)\n",
        "video_frames = tf.transpose(video_frames, (0, 1, 4, 2, 3))\n",
        "print('---before', video_frames.shape)\n",
        "#video_frames = video_frames.eval(session=tf.compat.v1.Session())\n",
        "print('----after', video_frames.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---before (1, 16, 3, 128, 160)\n",
            "----after (1, 16, 3, 128, 160)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2X8LGXx-KZW",
        "outputId": "31341f79-be75-4e8d-a5aa-22dcca7e053d"
      },
      "source": [
        "print(video_frames)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"transpose_1:0\", shape=(1, 16, 3, 128, 160), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8LXxNi39sTq",
        "outputId": "68989618-883d-4174-8747-ae3076a3c74a"
      },
      "source": [
        "print(video_frames.shape)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(16, 268, 268, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "BcNaR8cn9OYH",
        "outputId": "cb07c72b-fdc4-4883-d210-cf917efb9b33"
      },
      "source": [
        "#fake videoframe with np\n",
        "#model = test_model\n",
        "#x1 = test_model.predict(video_frames, batch_size=1)\n",
        "# inspect x1 and its size\n",
        "import cv2\n",
        "video_frames = video_frames / 255.0\n",
        "#video_frames = cv2.imread(video)\n",
        "\n",
        "video_frames = cv2.resize(video_frames, (128, 160))\n",
        "video_frames = np.expand_dims(video_frames, axis = 0)\n",
        "video_frames = np.transpose(video_frames, (0, 1, 4, 2, 3))\n",
        "print('---before', video_frames.shape)\n",
        "#video_frames = video_frames.eval(session=tf.compat.v1.Session())\n",
        "print('----after', video_frames.shape)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "error",
          "ename": "error",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-b2906103f198>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mvideo_frames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvideo_frames\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mvideo_frames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideo_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m160\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mvideo_frames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideo_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mvideo_frames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideo_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31merror\u001b[0m: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/resize.cpp:3363: error: (-215:Assertion failed) !dsize.empty() in function 'resize'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "-Xe88BqP30oV",
        "outputId": "a7d265fa-6415-43ed-ec58-608030c6a744"
      },
      "source": [
        "layer_errors = test_model.predict(video_frames, batch_size=1)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-4d5ed5f1e8c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlayer_errors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideo_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1460\u001b[0m                                             \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1461\u001b[0m                                             \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1462\u001b[0;31m                                             callbacks=callbacks)\n\u001b[0m\u001b[1;32m   1463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1464\u001b[0m     def train_on_batch(self, x, y,\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mpredict_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps, callbacks)\u001b[0m\n\u001b[1;32m    248\u001b[0m                                     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m                                     \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m                                     steps_name='steps')\n\u001b[0m\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[0;31m# Check if callbacks have not been already configured\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/keras/engine/training_utils.py\u001b[0m in \u001b[0;36mcheck_num_samples\u001b[0;34m(ins, batch_size, steps, steps_name)\u001b[0m\n\u001b[1;32m    569\u001b[0m             raise ValueError(\n\u001b[1;32m    570\u001b[0m                 \u001b[0;34m'If your data is in the form of symbolic tensors, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m                 \u001b[0;34m'you should specify the `'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msteps_name\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'` argument '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m                 \u001b[0;34m'(instead of the `batch_size` argument, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m                 \u001b[0;34m'because symbolic tensors are expected to produce '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: If your data is in the form of symbolic tensors, you should specify the `steps` argument (instead of the `batch_size` argument, because symbolic tensors are expected to produce batches of input data)."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_18RINht3O0t",
        "outputId": "1f451634-64d9-4081-8e4c-d952cd8e7a20"
      },
      "source": [
        "layer_errors.shape\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 16, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HFJTpu-4w_u",
        "outputId": "52d851f3-343f-4688-a2a4-b356257e9883"
      },
      "source": [
        "layer_errors[0]"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.18273176, 0.04453773, 0.03832496, 0.01487955],\n",
              "       [0.03596336, 0.03006915, 0.03490854, 0.02561901],\n",
              "       [0.0353268 , 0.02812615, 0.03014576, 0.02364295],\n",
              "       [0.03327681, 0.02769086, 0.02900193, 0.02112938],\n",
              "       [0.03540421, 0.02841974, 0.02832971, 0.02122713],\n",
              "       [0.03820645, 0.02774859, 0.02962496, 0.02311298],\n",
              "       [0.04542673, 0.02917672, 0.03243032, 0.02751301],\n",
              "       [0.05402784, 0.03109798, 0.03522575, 0.03250879],\n",
              "       [0.05613659, 0.03251697, 0.03065341, 0.02810185],\n",
              "       [0.05602853, 0.03102637, 0.03264998, 0.02835025],\n",
              "       [0.0441821 , 0.03063592, 0.03025474, 0.02462369],\n",
              "       [0.04349147, 0.02971124, 0.03365203, 0.02541041],\n",
              "       [0.05160765, 0.03078336, 0.0348851 , 0.02923952],\n",
              "       [0.04447443, 0.02999979, 0.03161296, 0.02622123],\n",
              "       [0.04105837, 0.03081635, 0.03400602, 0.02428697],\n",
              "       [0.03829812, 0.03174067, 0.03661424, 0.02509231]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLttXzTy4FWc"
      },
      "source": [
        "layer_errors = layer_errors.squeeze(axis=0)\n",
        "avg_activations = np.sum(layer_errors, axis=0)/float(num_frames)\n",
        "# act_tf[i] = avg_activations"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2f9NS2g4Utl",
        "outputId": "bd996076-4ff7-4285-fb41-b81330efff5a"
      },
      "source": [
        "avg_activations"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.05222758, 0.0308811 , 0.03264502, 0.02505994], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhKO4PIJ5n2A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGgFybtQDEOX"
      },
      "source": [
        "def get_activations_and_save(model, videos, save_dir, batch_size=1):\n",
        "    \"\"\" ---IGNORE-----\n",
        "    This function extracts the activations (features) of a specific layer of\n",
        "    a model to a set of videos and save them in a specified directory. Every\n",
        "    file is a list with a vector containing the activations of that layer to\n",
        "    a particular video. The activations are averaged over the frames of every\n",
        "    video. \n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    model :\n",
        "        pytorch model.\n",
        "    video_list : list\n",
        "        list containing path to all videos.\n",
        "    save_dir : str\n",
        "        save path for extracted activations.\n",
        "    layer : int\n",
        "        integer specifying layer number.\n",
        "    nparray(1100,4) 500 crahes\n",
        "    - perform all the computations here #pca here # averaging \n",
        "    - # averaging --> reduce the size of required numpy array \n",
        "    \n",
        "    For this loop --> after extracting actications what do we do?\n",
        "    \"\"\"\n",
        "    activations = np.zeros((len(videos), 4))\n",
        "    #act_tf = tf.convert_to_tensor(activations)\n",
        "    for i, video in enumerate(videos):\n",
        "        print(f\"Processing video #{i}\")\n",
        "\n",
        "        video_frames, num_frames = sample_video_from_mp4(video)\n",
        "\n",
        "        # preprocess video frames\n",
        "        video_frames = video_frames / 255.0\n",
        "        #replace tf with cv,np\n",
        "        video_frames = tf.image.resize(video_frames, (128, 160))\n",
        "        video_frames = tf.expand_dims(video_frames, axis = 0)\n",
        "        video_frames = tf.transpose(video_frames, (0, 1, 4, 2, 3))\n",
        "        video_frames = video_frames.eval(session=tf.compat.v1.Session())\n",
        "\n",
        "        # pass video frames through the model\n",
        "        layer_errors = model.predict(video_frames, batch_size)\n",
        "        del video_frames\n",
        "        # average layer activations/errors across frames\n",
        "        layer_errors = layer_errors.squeeze(axis=0)\n",
        "        avg_activations = np.sum(layer_errors, axis=0)/float(num_frames)\n",
        "        activations[i] = avg_activations\n",
        "        del avg_activations\n",
        "        #act_tf[i] = avg_activations\n",
        "        # 4 valued array, single dimension \n",
        "        #shape : num videos * 4\n",
        "        \n",
        "    return activations\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OyfVTIQowztB"
      },
      "source": [
        "# Extra"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-k_7XoAwcXi"
      },
      "source": [
        "activations_dir = \"/content/activations_prednet\"\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQPNIy3a58rd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcKF9fWF58zW",
        "outputId": "fb3aea2f-c417-48a0-805b-cb9c4baac391"
      },
      "source": [
        "ls"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mactivations_prednet\u001b[0m/              \u001b[01;34mmodel_data_keras2\u001b[0m/        \u001b[01;34msample_data\u001b[0m/\n",
            "\u001b[01;34mAlgonautsVideos268_All_30fpsmax\u001b[0m/  \u001b[01;34mparticipants_data_v2021\u001b[0m/\n",
            "class_names_ImageNet.txt          participants_data.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBa2ODTewhaD"
      },
      "source": [
        "import os \n"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lQGPTVKztzP"
      },
      "source": [
        ""
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qC8HKtjqwjB1"
      },
      "source": [
        "len(os.listdir(activations_dir))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-E0MsJBIx_Rh",
        "outputId": "01786b7b-3342-4cf1-b1f4-6d0fa0ac10eb"
      },
      "source": [
        "len(video_list)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1102"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vVclD15w3gZ"
      },
      "source": [
        "# Contd"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6i6DQ77M_jYo",
        "outputId": "01812aa0-428a-45f7-896d-e25b604da471"
      },
      "source": [
        "len(video_list)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1102"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7Qn3osf_lEL"
      },
      "source": [
        "video_list = video_list[0:20]"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wFsbq-g_pzf",
        "outputId": "ee6a3cfb-26be-4447-bfd8-cc571a300cf3"
      },
      "source": [
        "len(video_list)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "baNFbPRam4ek",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "963ba4e1-2f86-4a6d-b311-29d7b25af08e"
      },
      "source": [
        "# create saving directory for activations\n",
        "activations_dir = \"/content/activations_prednet_test\"\n",
        "if not os.path.exists(activations_dir):\n",
        "  os.makedirs(activations_dir)\n",
        "\n",
        "activations = get_activations_and_save(test_model, video_list, activations_dir)\n",
        "\n",
        "#get activations 1102*4"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processing video #0\n",
            "Processing video #1\n",
            "Processing video #2\n",
            "Processing video #3\n",
            "Processing video #4\n",
            "Processing video #5\n",
            "Processing video #6\n",
            "Processing video #7\n",
            "Processing video #8\n",
            "Processing video #9\n",
            "Processing video #10\n",
            "Processing video #11\n",
            "Processing video #12\n",
            "Processing video #13\n",
            "Processing video #14\n",
            "Processing video #15\n",
            "Processing video #16\n",
            "Processing video #17\n",
            "Processing video #18\n",
            "Processing video #19\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqIlr_EN6Fw-"
      },
      "source": [
        "# activations np array --> linear regression directly --> "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmnoKkpa_3EV",
        "outputId": "23d092ae-43c4-49f9-9bbe-5b3fb5edfbd3"
      },
      "source": [
        "activations"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04656222, 0.02843931, 0.02900872, 0.02011135],\n",
              "       [0.05222758, 0.0308811 , 0.03264502, 0.02505994],\n",
              "       [0.02460087, 0.02255601, 0.02020676, 0.01327849],\n",
              "       [0.05272121, 0.02993439, 0.03023641, 0.02394617],\n",
              "       [0.05854285, 0.03138214, 0.03596036, 0.02868075],\n",
              "       [0.02890898, 0.02878191, 0.02650614, 0.01797419],\n",
              "       [0.03912688, 0.0309162 , 0.03153494, 0.02377036],\n",
              "       [0.02125715, 0.02249834, 0.01747232, 0.01427237],\n",
              "       [0.02885841, 0.02138134, 0.0212229 , 0.01685181],\n",
              "       [0.03994235, 0.03061569, 0.03084563, 0.02184075],\n",
              "       [0.03173997, 0.02888   , 0.0281224 , 0.01764039],\n",
              "       [0.04498976, 0.02949046, 0.03087679, 0.01906163],\n",
              "       [0.03406916, 0.02485669, 0.0263645 , 0.02143869],\n",
              "       [0.03986578, 0.02885304, 0.03103199, 0.02313154],\n",
              "       [0.03696527, 0.02605534, 0.02575685, 0.01894011],\n",
              "       [0.03897932, 0.02575295, 0.02714255, 0.02026535],\n",
              "       [0.03589629, 0.02751032, 0.025687  , 0.01673773],\n",
              "       [0.05832988, 0.03242037, 0.03742404, 0.03164171],\n",
              "       [0.03432915, 0.02656821, 0.02564769, 0.02011506],\n",
              "       [0.03264977, 0.02450755, 0.02842223, 0.01938319]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scnuhATSDJX9"
      },
      "source": [
        "## Perform PCA on activations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KJibw6_DKz-"
      },
      "source": [
        "def do_PCA_and_save(activations_dir, save_dir): \n",
        "    \"\"\"This function preprocesses a neural network's features using PCA and\n",
        "    save the results in a specified directory.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    activations_dir : str\n",
        "        save path for extracted activations.\n",
        "    save_dir : str\n",
        "        save path for extracted PCA features.\n",
        "    \"\"\"\n",
        "\n",
        "    # number of PCA components\n",
        "    n_components = 100     \n",
        "        \n",
        "    # get path of activations of a layer to all videos\n",
        "    activations_file_list = glob.glob(activations_dir + '/*.npy')\n",
        "    # sort activations in ascending order\n",
        "    activations_file_list.sort()\n",
        "    # load one activation to later get dimension\n",
        "    feature_dim = np.load(activations_file_list[0], allow_pickle=True)[0][0].shape[0]\n",
        "        \n",
        "    # initialise data matrix for PCA - #videos x layer dimension\n",
        "    x = np.zeros((len(activations_file_list), feature_dim))\n",
        "        \n",
        "    # fill matrix with activations of every video (for a given layer)\n",
        "    for i, video_activations in enumerate(activations_file_list):\n",
        "        temp = np.load(video_activations, allow_pickle=True)[0][0].detach().numpy()\n",
        "        x[i,:] = temp\n",
        "        \n",
        "    # train-test split\n",
        "    x_train = x[:1000,:]\n",
        "    x_test = x[1000:,:]\n",
        "\n",
        "    print(\"Scaling data\")\n",
        "    # scale training and test sets\n",
        "    x_train = StandardScaler().fit_transform(x_train)\n",
        "    x_test = StandardScaler().fit_transform(x_test)\n",
        "\n",
        "    print(\"Fitting PCA\")\n",
        "    # find principal components of training set\n",
        "    pca = PCA(n_components = n_components, random_state = seed)\n",
        "    pca.fit(x_train)\n",
        "\n",
        "    print(\"Projecting data onto components\")\n",
        "    # project data onto components - #videos x #components \n",
        "    x_train = pca.transform(x_train)\n",
        "    x_test = pca.transform(x_test)\n",
        "\n",
        "    print(\"Saving\")\n",
        "    # create saving directory\n",
        "    train_save_path = os.path.join(save_dir, \"train_layer_19\")  ############ SPECIFY LAYER NUMBER #######\n",
        "    test_save_path = os.path.join(save_dir, \"test_layer_19\")\n",
        "\n",
        "    # save results\n",
        "    np.save(train_save_path, x_train)\n",
        "    np.save(test_save_path, x_test)\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTG4hqsGDORl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5edad82d-6652-42b8-8602-6b958400e9d3"
      },
      "source": [
        "# create saving directory for pca features\n",
        "pca_dir = '/content/pca_activations'\n",
        "if not os.path.exists(pca_dir):\n",
        "  os.makedirs(pca_dir)\n",
        "\n",
        "# call function to get dimensionality-reduced activations\n",
        "do_PCA_and_save(activations_dir, pca_dir)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scaling data\n",
            "Fitting PCA\n",
            "Projecting data onto components\n",
            "Saving\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJeWCZ3ODQD7"
      },
      "source": [
        "# **Regression on fMRI data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2WkeShVDSW0"
      },
      "source": [
        "#@title Helper functions for data loading\n",
        "\n",
        "\n",
        "# loading function for pickle ROI file\n",
        "def load_dict(filename_):\n",
        "    # read file in bytes\n",
        "    with open(filename_, 'rb') as f:\n",
        "        # create pickle object\n",
        "        u = pickle._Unpickler(f)\n",
        "        # # change encoding appropriate for numpy array\n",
        "        u.encoding = 'latin1'\n",
        "        # load it\n",
        "        ret_di = u.load()\n",
        "        \n",
        "    return ret_di\n",
        "\n",
        "\n",
        "def get_fmri(fmri_dir, sub, ROI):\n",
        "    \"\"\"This function loads fMRI data into a numpy array for a given\n",
        "    participant and ROI.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    fmri_dir : str\n",
        "        path to fMRI data.\n",
        "    sub : str\n",
        "        participant number path\n",
        "    ROI : str\n",
        "        name of ROI.\n",
        "\n",
        "    Returns\n",
        "    ---------\n",
        "    np.array\n",
        "        matrix of dimensions #train_vids x #repetitions x #voxels\n",
        "        containing fMRI responses to train videos of a given ROI.\n",
        "    \"\"\"\n",
        "\n",
        "    # Load ROI data\n",
        "    ROI_file = os.path.join(fmri_dir, sub, ROI + \".pkl\")\n",
        "    ROI_data = load_dict(ROI_file)\n",
        "\n",
        "    # averaging ROI data across repetitions\n",
        "    ROI_data_train = np.mean(ROI_data[\"train\"], axis = 1)\n",
        "\n",
        "    return ROI_data_train\n",
        "\n",
        "\n",
        "def get_activations(activations_dir, layer_name):\n",
        "    \"\"\"This function loads neural network features/activations (preprocessed\n",
        "    using PCA) into a numpy array according to a given layer.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    activations_dir : str\n",
        "        path to PCA/processed neural network features\n",
        "    layer_name : str\n",
        "        which layer of the neural network to load\n",
        "    \n",
        "    Returns\n",
        "    ---------\n",
        "    train_activations : np.array\n",
        "        matrix of dimensions #train_vids x #pca_components\n",
        "        containing activations of train videos\n",
        "    \"\"\"\n",
        "\n",
        "    # load training and test file of a given layer\n",
        "    train_file = os.path.join(activations_dir,\"train_\" + layer_name + \".npy\")\n",
        "    #test_file = os.path.join(activations_dir,\"test_\" + layer_name + \".npy\")\n",
        "    train_activations = np.load(train_file)\n",
        "    #test_activations = np.load(test_file)\n",
        "\n",
        "    # scale training and test activations\n",
        "    scaler = StandardScaler()\n",
        "    train_activations = scaler.fit_transform(train_activations)\n",
        "    #test_activations = scaler.fit_transform(test_activations)\n",
        "\n",
        "    return train_activations"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAlkjEyFD-gf",
        "outputId": "4f8577d2-d424-473c-f4ee-f1fb588f43db"
      },
      "source": [
        "len(os.listdir(activations_dir))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMQ4JZPHDWpg"
      },
      "source": [
        "#@title Correlation function for model validation\n",
        "\n",
        "def vectorized_correlation(x,y):\n",
        "    dim = 0\n",
        "\n",
        "    centered_x = x - x.mean(axis=dim, keepdims=True)\n",
        "    centered_y = y - y.mean(axis=dim, keepdims=True)\n",
        "\n",
        "    covariance = (centered_x * centered_y).sum(axis=dim, keepdims=True)\n",
        "\n",
        "    bessel_corrected_covariance = covariance / (x.shape[dim] - 1)\n",
        "\n",
        "    x_std = x.std(axis=dim, keepdims=True)+1e-8\n",
        "    y_std = y.std(axis=dim, keepdims=True)+1e-8\n",
        "\n",
        "    corr = bessel_corrected_covariance / (x_std * y_std)\n",
        "\n",
        "    return corr.ravel()"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qYsbDKnAk5j"
      },
      "source": [
        "'''def perform_encoding(pca_dir, fmri_dir, results_dir, layer, sub, ROI,\n",
        "                     batch_size=1000):\n",
        "  \"\"\"This function linearly regresses pca-reduced features/activations of a\n",
        "  given layer of a neural network to the fMRI activity of a given brain region\n",
        "  (ROI) in a given subject. The fitted model is used to predict part of the\n",
        "  training set (validation mode). The model predictions are saved in a specified\n",
        "  directory. \n",
        "  \n",
        "  # Arguments\n",
        "  -------------\n",
        "  pca_dir : str\n",
        "      path to PCA features.\n",
        "  fmri_dir : str\n",
        "      path to fMRI data.\n",
        "  results_dir : str\n",
        "      saving directory for results.\n",
        "  layer : str\n",
        "      layer name from which to extract activations.\n",
        "  sub : str\n",
        "      participant number path.\n",
        "  ROI : str\n",
        "      region of interest (brain region) from which to extract fMRI data.\n",
        "  batch_size : int\n",
        "      Number of voxels processed when fitting the linear regressor. 1000 by\n",
        "      default.\n",
        "\n",
        "  \"\"\"\n",
        "\n",
        "  # load train activations\n",
        "  train_activations = get_activations(pca_dir, layer)\n",
        "  \n",
        "  # load fMRI data for a given subject and ROI\n",
        "  fmri_train_all = get_fmri(fmri_dir, sub, ROI)\n",
        "  # get number of voxels for given ROI\n",
        "  num_voxels = fmri_train_all.shape[1]\n",
        "\n",
        "  # create training and validation sets\n",
        "  val_activations = train_activations[900:,:]       ################ adjust splits according to #videos ###############\n",
        "  train_activations = train_activations[:900,:]    \n",
        "  fmri_train = fmri_train_all[:900,:]\n",
        "  fmri_val = fmri_train_all[900:,:]      \n",
        "\n",
        "  # initialise results - #validation videos x #voxels\n",
        "  pred_fmri = np.zeros_like(fmri_val)\n",
        "  pred_fmri_save_path = os.path.join(results_dir)  ##### removed subject and ROI #####\n",
        "\n",
        "  # perform multiple multivariate regression\n",
        "  reg = MultiOutputRegressor(LinearRegression()).fit(train_activations, fmri_train)\n",
        "  pred_fmri = reg.predict(val_activations)\n",
        "  \n",
        "  # correlation between validation predictions and ground truth\n",
        "  corr = vectorized_correlation(fmri_val, pred_fmri)\n",
        "  print(\"----------------------------------------------------------------------------\")\n",
        "  # get mean correlation\n",
        "  voxelwise_corr = round(corr.mean(), 6)\n",
        "  print(\"Mean correlation for ROI : \",ROI,\" in \",sub,\" using \",layer,\" is :\", voxelwise_corr)\n",
        "  \n",
        "  # save results\n",
        "  np.save(pred_fmri_save_path, pred_fmri)\n",
        "\n",
        "  return voxelwise_corr'''\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tubaR1MHAfml"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42Gdl4cwDcS-"
      },
      "source": [
        "def perform_encoding(activations, fmri_dir, results_dir, sub, ROI, batch_size=2):\n",
        "\n",
        "  \"\"\"This function linearly regresses pca-reduced features/activations of a\n",
        "  given layer of a neural network to the fMRI activity of a given brain region\n",
        "  (ROI) in a given subject. The fitted model is used to predict part of the\n",
        "  training set (validation mode). The model predictions are saved in a specified\n",
        "  directory. \n",
        "  \n",
        "  # Arguments\n",
        "  -------------\n",
        "  pca_dir : str\n",
        "      path to PCA features.\n",
        "  fmri_dir : str\n",
        "      path to fMRI data.\n",
        "  results_dir : str\n",
        "      saving directory for results.\n",
        "  layer : str\n",
        "      layer name from which to extract activations.\n",
        "  sub : str\n",
        "      participant number path.\n",
        "  ROI : str\n",
        "      region of interest (brain region) from which to extract fMRI data.\n",
        "  batch_size : int\n",
        "      Number of voxels processed when fitting the linear regressor. 1000 by\n",
        "      default.\n",
        "\n",
        "  \"\"\"\n",
        "\n",
        "  # load train activations\n",
        "  #train_activations = get_activations(pca_dir, layer)\n",
        "  train_activations = activations\n",
        "\n",
        "  # load fMRI data for a given subject and ROI\n",
        "  \n",
        "  fmri_train_all = get_fmri(fmri_dir, sub, ROI)\n",
        "  fmri_train_all = fmri_train_all[0:20]\n",
        "  # get number of voxels for given ROI\n",
        "  num_voxels = fmri_train_all.shape[1]\n",
        "\n",
        "  # create training and validation sets\n",
        "  train_int = 17\n",
        "  val_activations = train_activations[train_int:,:]       ################ adjust splits according to #videos ###############\n",
        "  train_activations = train_activations[:train_int,:]    \n",
        "  fmri_train = fmri_train_all[:train_int,:]\n",
        "  fmri_val = fmri_train_all[train_int:,:]   \n",
        "\n",
        "  # initialise results - #validation videos x #voxels\n",
        "  pred_fmri = np.zeros_like(fmri_val)\n",
        "  pred_fmri_save_path = os.path.join(results_dir)  ##### removed subject and ROI #####\n",
        "\n",
        "  # perform multiple multivariate regression\n",
        "  reg = MultiOutputRegressor(LinearRegression()).fit(train_activations, fmri_train)\n",
        "  pred_fmri = reg.predict(val_activations)\n",
        "  \n",
        "  # correlation between validation predictions and ground truth\n",
        "  #issue with fmri_val and pred_fmri\n",
        "  print(fmri_val.shape)\n",
        "  print(pred_fmri.shape)\n",
        "  corr = vectorized_correlation(fmri_val, pred_fmri)\n",
        "  print(\"----------------------------------------------------------------------------\")\n",
        "  # get mean correlation\n",
        "  voxelwise_corr = round(corr.mean(), 6)\n",
        "  print(\"Mean correlation for ROI : \",ROI,\" in \",sub,\" using  is :\", voxelwise_corr)\n",
        "  \n",
        "  # save results\n",
        "  np.save(pred_fmri_save_path, pred_fmri)\n",
        "\n",
        "  return voxelwise_corr\n"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CwJZ9aDpERmt",
        "outputId": "6bcb75e2-260c-45da-8821-f23541e7b29b"
      },
      "source": [
        ""
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['sub04', 'sub02', 'sub06', 'sub08', 'sub10', 'sub07', 'sub05', 'sub01', 'sub03', 'sub09']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKdXhjfnAH1Y"
      },
      "source": [
        "# def perform_encoding(activations, fmri_dir, results_dir, sub, ROI, batch_size=1000):"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZzgBLKzDeyH"
      },
      "source": [
        "# specify create fmri directory\n",
        "fmri_dir = '/content/participants_data_v2021/mini_track' \n",
        "\n",
        "# create saving directory for results\n",
        "prediction_dir = '/content/model_predictions'\n",
        "if not os.path.exists(prediction_dir):\n",
        "  os.makedirs(prediction_dir)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0o5dvac9DgXL"
      },
      "source": [
        "# list of subjects, ROIs, model and layers\n",
        "subs = [\"sub01\",\"sub02\",\"sub03\",\"sub04\",\"sub05\",\"sub06\",\"sub07\",\"sub08\",\"sub09\",\"sub10\"]  \n",
        "ROIs = [\"V1\",\"V2\",\"V3\",\"V4\",\"LOC\",\"EBA\",\"FFA\",\"STS\",\"PPA\"]"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSFgV72nDiGH"
      },
      "source": [
        "##############################\n",
        "#### CHANGE LAYER NUMBER #####\n",
        "# layer = 'layer_19'\n",
        "##############################"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3q93Zi7CAYW"
      },
      "source": [
        "# def perform_encoding(activations, fmri_dir, results_dir, sub, ROI, batch_size=1000):"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlGHxbcxDjnx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "177c870a-2209-4e9c-b181-4a21bbefd878"
      },
      "source": [
        "# loop across subjects and layers\n",
        "voxelwise_corrs = np.zeros((len(subs), len(ROIs)))\n",
        "\n",
        "for i, sub in enumerate(subs):\n",
        "  for j, ROI in enumerate(ROIs):\n",
        "    # make prediction\n",
        "    print(\"Starting ROI: \", ROI, \"sub: \", sub)\n",
        "    voxelwise_corrs[i, j] = perform_encoding(activations, fmri_dir, prediction_dir, sub = sub, ROI = ROI)\n",
        "    print (\"Completed ROI: \", ROI, \"sub: \", sub)\n",
        "    print(\"----------------------------------------------------------------------------\")\n",
        "\n",
        "# create saving directory\n",
        "all_prediction_dir = os.path.join(prediction_dir, 'mean_correlation_all_subs_all_ROIs_with_')\n",
        "np.save(all_prediction_dir, voxelwise_corrs)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting ROI:  V1 sub:  sub01\n",
            "(3, 232)\n",
            "(3, 232)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub01  using  is : -0.47494\n",
            "Completed ROI:  V1 sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub01\n",
            "(3, 231)\n",
            "(3, 231)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub01  using  is : -0.449836\n",
            "Completed ROI:  V2 sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub01\n",
            "(3, 261)\n",
            "(3, 261)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub01  using  is : -0.472619\n",
            "Completed ROI:  V3 sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub01\n",
            "(3, 107)\n",
            "(3, 107)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub01  using  is : -0.54841\n",
            "Completed ROI:  V4 sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub01\n",
            "(3, 1843)\n",
            "(3, 1843)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub01  using  is : -0.240127\n",
            "Completed ROI:  LOC sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub01\n",
            "(3, 351)\n",
            "(3, 351)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub01  using  is : -0.239434\n",
            "Completed ROI:  EBA sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub01\n",
            "(3, 68)\n",
            "(3, 68)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub01  using  is : -0.259684\n",
            "Completed ROI:  FFA sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub01\n",
            "(3, 341)\n",
            "(3, 341)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub01  using  is : -0.31544\n",
            "Completed ROI:  STS sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub01\n",
            "(3, 425)\n",
            "(3, 425)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub01  using  is : 0.025053\n",
            "Completed ROI:  PPA sub:  sub01\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub02\n",
            "(3, 285)\n",
            "(3, 285)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub02  using  is : 0.11517\n",
            "Completed ROI:  V1 sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub02\n",
            "(3, 231)\n",
            "(3, 231)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub02  using  is : 0.019019\n",
            "Completed ROI:  V2 sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub02\n",
            "(3, 270)\n",
            "(3, 270)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub02  using  is : -0.09363\n",
            "Completed ROI:  V3 sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub02\n",
            "(3, 95)\n",
            "(3, 95)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub02  using  is : -0.34441\n",
            "Completed ROI:  V4 sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub02\n",
            "(3, 1348)\n",
            "(3, 1348)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub02  using  is : -0.042427\n",
            "Completed ROI:  LOC sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub02\n",
            "(3, 183)\n",
            "(3, 183)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub02  using  is : 0.194482\n",
            "Completed ROI:  EBA sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub02\n",
            "(3, 157)\n",
            "(3, 157)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub02  using  is : -0.065449\n",
            "Completed ROI:  FFA sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub02\n",
            "(3, 421)\n",
            "(3, 421)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub02  using  is : -0.322745\n",
            "Completed ROI:  STS sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub02\n",
            "(3, 153)\n",
            "(3, 153)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub02  using  is : 0.196552\n",
            "Completed ROI:  PPA sub:  sub02\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub03\n",
            "(3, 164)\n",
            "(3, 164)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub03  using  is : -0.158608\n",
            "Completed ROI:  V1 sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub03\n",
            "(3, 271)\n",
            "(3, 271)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub03  using  is : -0.114643\n",
            "Completed ROI:  V2 sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub03\n",
            "(3, 270)\n",
            "(3, 270)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub03  using  is : -0.186967\n",
            "Completed ROI:  V3 sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub03\n",
            "(3, 111)\n",
            "(3, 111)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub03  using  is : 0.106046\n",
            "Completed ROI:  V4 sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub03\n",
            "(3, 1237)\n",
            "(3, 1237)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub03  using  is : -0.190042\n",
            "Completed ROI:  LOC sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub03\n",
            "(3, 376)\n",
            "(3, 376)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub03  using  is : -0.227304\n",
            "Completed ROI:  EBA sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub03\n",
            "(3, 80)\n",
            "(3, 80)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub03  using  is : -0.569707\n",
            "Completed ROI:  FFA sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub03\n",
            "(3, 278)\n",
            "(3, 278)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub03  using  is : -0.139765\n",
            "Completed ROI:  STS sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub03\n",
            "(3, 368)\n",
            "(3, 368)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub03  using  is : 0.045603\n",
            "Completed ROI:  PPA sub:  sub03\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub04\n",
            "(3, 176)\n",
            "(3, 176)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub04  using  is : -0.094643\n",
            "Completed ROI:  V1 sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub04\n",
            "(3, 209)\n",
            "(3, 209)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub04  using  is : 0.080295\n",
            "Completed ROI:  V2 sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub04\n",
            "(3, 212)\n",
            "(3, 212)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub04  using  is : 0.019289\n",
            "Completed ROI:  V3 sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub04\n",
            "(3, 117)\n",
            "(3, 117)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub04  using  is : -0.047334\n",
            "Completed ROI:  V4 sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub04\n",
            "(3, 1153)\n",
            "(3, 1153)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub04  using  is : 0.074737\n",
            "Completed ROI:  LOC sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub04\n",
            "(3, 368)\n",
            "(3, 368)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub04  using  is : 0.454699\n",
            "Completed ROI:  EBA sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub04\n",
            "(3, 210)\n",
            "(3, 210)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub04  using  is : -0.187674\n",
            "Completed ROI:  FFA sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub04\n",
            "(3, 398)\n",
            "(3, 398)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub04  using  is : 0.095541\n",
            "Completed ROI:  STS sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub04\n",
            "(3, 225)\n",
            "(3, 225)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub04  using  is : 0.156115\n",
            "Completed ROI:  PPA sub:  sub04\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub05\n",
            "(3, 326)\n",
            "(3, 326)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub05  using  is : -0.036443\n",
            "Completed ROI:  V1 sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub05\n",
            "(3, 196)\n",
            "(3, 196)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub05  using  is : -0.213753\n",
            "Completed ROI:  V2 sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub05\n",
            "(3, 176)\n",
            "(3, 176)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub05  using  is : -0.042123\n",
            "Completed ROI:  V3 sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub05\n",
            "(3, 73)\n",
            "(3, 73)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub05  using  is : 0.093112\n",
            "Completed ROI:  V4 sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub05\n",
            "(3, 1397)\n",
            "(3, 1397)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub05  using  is : -0.122895\n",
            "Completed ROI:  LOC sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub05\n",
            "(3, 309)\n",
            "(3, 309)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub05  using  is : 0.24497\n",
            "Completed ROI:  EBA sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub05\n",
            "(3, 69)\n",
            "(3, 69)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub05  using  is : -0.113658\n",
            "Completed ROI:  FFA sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub05\n",
            "(3, 219)\n",
            "(3, 219)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub05  using  is : -0.107512\n",
            "Completed ROI:  STS sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub05\n",
            "(3, 210)\n",
            "(3, 210)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub05  using  is : 0.103837\n",
            "Completed ROI:  PPA sub:  sub05\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub06\n",
            "(3, 286)\n",
            "(3, 286)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub06  using  is : 0.230049\n",
            "Completed ROI:  V1 sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub06\n",
            "(3, 281)\n",
            "(3, 281)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub06  using  is : 0.327227\n",
            "Completed ROI:  V2 sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub06\n",
            "(3, 229)\n",
            "(3, 229)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub06  using  is : 0.210764\n",
            "Completed ROI:  V3 sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub06\n",
            "(3, 108)\n",
            "(3, 108)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub06  using  is : -0.018816\n",
            "Completed ROI:  V4 sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub06\n",
            "(3, 1356)\n",
            "(3, 1356)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub06  using  is : 0.154102\n",
            "Completed ROI:  LOC sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub06\n",
            "(3, 308)\n",
            "(3, 308)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub06  using  is : 0.155273\n",
            "Completed ROI:  EBA sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub06\n",
            "(3, 119)\n",
            "(3, 119)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub06  using  is : -0.037164\n",
            "Completed ROI:  FFA sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub06\n",
            "(3, 173)\n",
            "(3, 173)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub06  using  is : 0.050172\n",
            "Completed ROI:  STS sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub06\n",
            "(3, 216)\n",
            "(3, 216)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub06  using  is : -0.159821\n",
            "Completed ROI:  PPA sub:  sub06\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub07\n",
            "(3, 195)\n",
            "(3, 195)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub07  using  is : 0.013873\n",
            "Completed ROI:  V1 sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub07\n",
            "(3, 189)\n",
            "(3, 189)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub07  using  is : 0.046201\n",
            "Completed ROI:  V2 sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub07\n",
            "(3, 174)\n",
            "(3, 174)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub07  using  is : 0.117626\n",
            "Completed ROI:  V3 sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub07\n",
            "(3, 55)\n",
            "(3, 55)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub07  using  is : 0.147408\n",
            "Completed ROI:  V4 sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub07\n",
            "(3, 1117)\n",
            "(3, 1117)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub07  using  is : 0.029001\n",
            "Completed ROI:  LOC sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub07\n",
            "(3, 101)\n",
            "(3, 101)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub07  using  is : 0.649362\n",
            "Completed ROI:  EBA sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub07\n",
            "(3, 89)\n",
            "(3, 89)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub07  using  is : 0.09631\n",
            "Completed ROI:  FFA sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub07\n",
            "(3, 80)\n",
            "(3, 80)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub07  using  is : 0.21726\n",
            "Completed ROI:  STS sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub07\n",
            "(3, 33)\n",
            "(3, 33)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub07  using  is : -0.130777\n",
            "Completed ROI:  PPA sub:  sub07\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub08\n",
            "(3, 300)\n",
            "(3, 300)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub08  using  is : 0.038219\n",
            "Completed ROI:  V1 sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub08\n",
            "(3, 238)\n",
            "(3, 238)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub08  using  is : 0.029926\n",
            "Completed ROI:  V2 sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub08\n",
            "(3, 223)\n",
            "(3, 223)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub08  using  is : -0.124714\n",
            "Completed ROI:  V3 sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub08\n",
            "(3, 85)\n",
            "(3, 85)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub08  using  is : -0.028163\n",
            "Completed ROI:  V4 sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub08\n",
            "(3, 1244)\n",
            "(3, 1244)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub08  using  is : 0.134851\n",
            "Completed ROI:  LOC sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub08\n",
            "(3, 55)\n",
            "(3, 55)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub08  using  is : 0.167311\n",
            "Completed ROI:  EBA sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub08\n",
            "(3, 163)\n",
            "(3, 163)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub08  using  is : -0.230808\n",
            "Completed ROI:  FFA sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub08\n",
            "(3, 306)\n",
            "(3, 306)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub08  using  is : -0.073654\n",
            "Completed ROI:  STS sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub08\n",
            "(3, 150)\n",
            "(3, 150)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub08  using  is : 0.129949\n",
            "Completed ROI:  PPA sub:  sub08\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub09\n",
            "(3, 271)\n",
            "(3, 271)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub09  using  is : 0.147089\n",
            "Completed ROI:  V1 sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub09\n",
            "(3, 265)\n",
            "(3, 265)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub09  using  is : -0.219862\n",
            "Completed ROI:  V2 sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub09\n",
            "(3, 245)\n",
            "(3, 245)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub09  using  is : -0.114874\n",
            "Completed ROI:  V3 sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub09\n",
            "(3, 94)\n",
            "(3, 94)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub09  using  is : 0.167585\n",
            "Completed ROI:  V4 sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub09\n",
            "(3, 1515)\n",
            "(3, 1515)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub09  using  is : -0.041802\n",
            "Completed ROI:  LOC sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub09\n",
            "(3, 191)\n",
            "(3, 191)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub09  using  is : 0.16599\n",
            "Completed ROI:  EBA sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub09\n",
            "(3, 76)\n",
            "(3, 76)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub09  using  is : -0.424398\n",
            "Completed ROI:  FFA sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub09\n",
            "(3, 346)\n",
            "(3, 346)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub09  using  is : -0.087319\n",
            "Completed ROI:  STS sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub09\n",
            "(3, 262)\n",
            "(3, 262)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub09  using  is : 0.059378\n",
            "Completed ROI:  PPA sub:  sub09\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V1 sub:  sub10\n",
            "(3, 238)\n",
            "(3, 238)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V1  in  sub10  using  is : 0.129178\n",
            "Completed ROI:  V1 sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V2 sub:  sub10\n",
            "(3, 249)\n",
            "(3, 249)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V2  in  sub10  using  is : 0.02198\n",
            "Completed ROI:  V2 sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V3 sub:  sub10\n",
            "(3, 188)\n",
            "(3, 188)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V3  in  sub10  using  is : 0.308091\n",
            "Completed ROI:  V3 sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  V4 sub:  sub10\n",
            "(3, 60)\n",
            "(3, 60)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  V4  in  sub10  using  is : -0.089279\n",
            "Completed ROI:  V4 sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  LOC sub:  sub10\n",
            "(3, 1034)\n",
            "(3, 1034)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  LOC  in  sub10  using  is : 0.145229\n",
            "Completed ROI:  LOC sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  EBA sub:  sub10\n",
            "(3, 162)\n",
            "(3, 162)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  EBA  in  sub10  using  is : 0.348063\n",
            "Completed ROI:  EBA sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  FFA sub:  sub10\n",
            "(3, 69)\n",
            "(3, 69)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  FFA  in  sub10  using  is : -0.001334\n",
            "Completed ROI:  FFA sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  STS sub:  sub10\n",
            "(3, 120)\n",
            "(3, 120)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  STS  in  sub10  using  is : 0.461236\n",
            "Completed ROI:  STS sub:  sub10\n",
            "----------------------------------------------------------------------------\n",
            "Starting ROI:  PPA sub:  sub10\n",
            "(3, 165)\n",
            "(3, 165)\n",
            "----------------------------------------------------------------------------\n",
            "Mean correlation for ROI :  PPA  in  sub10  using  is : 0.110206\n",
            "Completed ROI:  PPA sub:  sub10\n",
            "----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "x2zbcAegGjoQ",
        "outputId": "09e76e93-c06a-461a-9f37-36f83ae60848"
      },
      "source": [
        "#ours \n",
        "subjs_mean = np.mean(voxelwise_corrs, axis = 0)\n",
        "subjs_std = np.std(voxelwise_corrs, axis = 0)\n",
        "plt.plot(ROIs, subjs_mean)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8550989350>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyEWbmQyDlla",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e16e0587-5dcf-488f-ed54-6fd2639e3e03"
      },
      "source": [
        "subjs_mean = np.mean(voxelwise_corrs, axis = 0)\n",
        "subjs_std = np.std(voxelwise_corrs, axis = 0)\n",
        "plt.plot(ROIs, subjs_mean)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fdb67a2a290>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "_64u_YmsGwmw",
        "outputId": "95854422-8256-4775-9c60-0d518d4a6b32"
      },
      "source": [
        "#ours \n",
        "plt.plot(ROIs, subjs_mean, linewidth=2)\n",
        "plt.title('FC #3', fontsize = 20)\n",
        "plt.xlabel('Region of interest', fontsize=18, labelpad=15)\n",
        "plt.ylabel('Correlation', fontsize=18, labelpad=15)\n",
        "plt.ylim([0, 0.25])\n",
        "plt.savefig('Prediction_with_VGG19_layer_19.pdf', bbox_inches='tight')"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "HtuF2y6qWbC2",
        "outputId": "639f7282-d34a-4cdb-c06e-0ad529c0bab2"
      },
      "source": [
        "plt.plot(ROIs, subjs_mean, linewidth=2)\n",
        "plt.title('FC #3', fontsize = 20)\n",
        "plt.xlabel('Region of interest', fontsize=18, labelpad=15)\n",
        "plt.ylabel('Correlation', fontsize=18, labelpad=15)\n",
        "plt.ylim([0, 0.25])\n",
        "plt.savefig('Prediction_with_VGG19_layer_19.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iac36Nq-Guyz"
      },
      "source": [
        "# huge tensor array , memory fixing of overusage"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}