guides/inference.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "AB2A-pf9kzzL"
      },
      "outputs": [],
      "source": [
        "# RUN THIS CELL EVERY RESET RUNTIME\n",
        "WORKDIR='/content/AIC2022-VER'\n",
        "DATADIR='/content/AIC2022-VER/data_sample' # replace this path\n",
        "GITHUB_TOKEN=''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUEeZwdFgNHr"
      },
      "source": [
        "## Clone repo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ci-7ESN_Y5-"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade --no-cache-dir gdown google-cloud-storage python-Levenshtein webcolors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6noGGK9fiyBW",
        "outputId": "1776c284-3b3b-4611-b4fa-e527a74aa21c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'AIC2022-VER'...\n",
            "remote: Enumerating objects: 2253, done.\u001b[K\n",
            "remote: Counting objects: 100% (2253/2253), done.\u001b[K\n",
            "remote: Compressing objects: 100% (1194/1194), done.\u001b[K\n",
            "remote: Total 2253 (delta 1132), reused 2021 (delta 951), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (2253/2253), 12.81 MiB | 3.79 MiB/s, done.\n",
            "Resolving deltas: 100% (1132/1132), done.\n"
          ]
        }
      ],
      "source": [
        "REPO_URL=f'https://{GITHUB_TOKEN}@github.com/nhtlongcs/AIC2022-VER/'\n",
        "!git clone $REPO_URL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UfN3fZBVpOJ_"
      },
      "outputs": [],
      "source": [
        "%cd $WORKDIR\n",
        "!git checkout add-rfm\n",
        "!git reset --hard HEAD\n",
        "!git pull"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "TLDc3bwUk5PZ"
      },
      "outputs": [],
      "source": [
        "!chmod +x $WORKDIR/tools/*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "pxPgv5J0lb_N"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "%cd $WORKDIR\n",
        "!pip install faiss-gpu\n",
        "!pip install -r requirements.txt\n",
        "!pip install -e . "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "79AEtMK2odjV"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!python -m spacy download en_core_web_sm\n",
        "!python -m nltk.downloader punkt wordnet omw-1.4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-FA4My8lYiN"
      },
      "source": [
        "## Download checkpoints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "PpSaOVemhdCh"
      },
      "outputs": [],
      "source": [
        "import gdown \n",
        "def download(token, output: str):\n",
        "    url = f'https://drive.google.com/uc?id={token}'\n",
        "    gdown.download(url, output, quiet=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qv0kRot_hrqh",
        "outputId": "b053ce92-090c-446c-97ef-ec63029e9127"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/AIC2022-VER\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1eylDX3tAJ2G0xhQsdOhU5uT1qH6SdzYK\n",
            "To: /content/AIC2022-VER/retrieval-checkpoints.zip\n",
            "100%|██████████| 10.0G/10.0G [01:44<00:00, 95.6MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1VQ5vERytyTqDKlu2lM_ehk8BlgZ6wHgi\n",
            "To: /content/AIC2022-VER/srl-checkpoints.zip\n",
            "100%|██████████| 120M/120M [00:01<00:00, 82.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "%cd $WORKDIR\n",
        "download('1eylDX3tAJ2G0xhQsdOhU5uT1qH6SdzYK', 'retrieval-checkpoints.zip')\n",
        "download('1VQ5vERytyTqDKlu2lM_ehk8BlgZ6wHgi', 'srl-checkpoints.zip')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGzcDqnuie69",
        "outputId": "2d44497b-7043-4c15-ff8e-e0d00be05859"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/AIC2022-VER/srl-checkpoints.zip\n",
            "   creating: artifacts/\n",
            "   creating: artifacts/vehicle/\n",
            "   creating: artifacts/color/\n",
            "  inflating: artifacts/vehicle/model.ckpt  \n",
            "  inflating: artifacts/color/model.ckpt  \n",
            "Archive:  /content/AIC2022-VER/retrieval-checkpoints.zip\n",
            "   creating: artifacts/model-2xjz0ux8:v25/\n",
            "  inflating: artifacts/model-2xjz0ux8:v25/model.ckpt  \n",
            "   creating: artifacts/model-1ityga7d:v20/\n",
            "  inflating: artifacts/model-1ityga7d:v20/model.ckpt  \n",
            "   creating: artifacts/model-3e6jnmrm:v16/\n",
            "  inflating: artifacts/model-3e6jnmrm:v16/model.ckpt  \n",
            "   creating: artifacts/model-55pfbgaf:v15/\n",
            "  inflating: artifacts/model-55pfbgaf:v15/model.ckpt  \n",
            "   creating: artifacts/model-3r4fsrlp:v29/\n",
            "  inflating: artifacts/model-3r4fsrlp:v29/model.ckpt  \n",
            "   creating: artifacts/model-3kto46es:v7/\n",
            "  inflating: artifacts/model-3kto46es:v7/model.ckpt  \n",
            "   creating: artifacts/model-5h72x53e:v18/\n",
            "  inflating: artifacts/model-5h72x53e:v18/model.ckpt  \n"
          ]
        }
      ],
      "source": [
        "!unzip $WORKDIR/srl-checkpoints.zip\n",
        "!unzip $WORKDIR/retrieval-checkpoints.zip\n",
        "!rm -rf $WORKDIR/srl-checkpoints.zip\n",
        "!rm -rf $WORKDIR/retrieval-checkpoints.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAvtoTtYgPM9"
      },
      "source": [
        "## Preprocess data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTNehmexk2ql",
        "outputId": "c27a9a4c-c951-43ad-f2f5-42990592f056"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/AIC2022-VER\n",
            "Extracting motion map ...\n",
            "3it [00:01,  2.16it/s]\n",
            "6it [00:05,  1.04it/s]\n",
            "DONE\n",
            "CPU times: user 103 ms, sys: 29 ms, total: 132 ms\n",
            "Wall time: 8.67 s\n"
          ]
        }
      ],
      "source": [
        "%%time \n",
        "%cd $WORKDIR\n",
        "!./tools/preproc_motion.sh $DATADIR/meta/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbYcOeWjiHYU",
        "outputId": "7f330034-8611-44e6-9da6-5dcd67f87492"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/AIC2022-VER\n",
            "Augmenting data ...\n",
            "100% 4/4 [00:00<00:00, 15.48it/s]\n",
            "DONE\n",
            "100% 2/2 [00:00<00:00, 27.57it/s]\n",
            "DONE\n",
            "Extracting SRL data ...\n",
            "Load data_info successfully\n",
            "downloading: 100%|##########| 387M/387M [00:09<00:00, 42.0MiB/s]\n",
            "Downloading: 100% 28.0/28.0 [00:00<00:00, 18.7kB/s]\n",
            "Downloading: 100% 570/570 [00:00<00:00, 404kB/s]\n",
            "Downloading: 100% 226k/226k [00:00<00:00, 683kB/s]\n",
            "Downloading: 100% 455k/455k [00:00<00:00, 1.10MB/s]\n",
            "Downloading: 100% 420M/420M [00:08<00:00, 49.4MB/s]\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Load other info successfully\n",
            "100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "0it [00:00, ?it/s]\n",
            "Load data_info successfully\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Load other info successfully\n",
            "100% 2/2 [00:01<00:00,  1.87it/s]\n",
            "0it [00:00, ?it/s]\n",
            "DONE\n",
            "mkdir: cannot create directory ‘/content/AIC2022-VER/data_sample/meta/srl’: File exists\n",
            "Extracting SRL data [action] ...\n",
            "{'turn': 0, 'curve': 0, 'bare': 0, 'stop': 1, 'park': 1, 'wait': 1, 'pause': 1, 'run': 2, 'go': 2, 'drive': 2, 'keep': 2, 'cross': 2, 'move': 2, 'continue': 2, 'travel': 2, 'proceed': 2, 'merge': 2, 'switch': 2, 'change': 2}\n",
            "100% 4/4 [00:00<00:00, 4009.85it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/action directory\n",
            "0 fail queries: []\n",
            "100% 4/4 [00:00<00:00, 5531.56it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/action directory\n",
            "0 fail queries: []\n",
            "100% 2/2 [00:00<00:00, 2966.27it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/action directory\n",
            "0 fail queries: []\n",
            "100% 2/2 [00:00<00:00, 3687.30it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/action directory\n",
            "0 fail queries: []\n",
            "DONE\n",
            "Extracting SRL data [color] ...\n",
            "{'gray': 0, 'silver': 0, 'gold': 1, 'tan': 1, 'beige': 1, 'brown': 1, 'yellow': 1, 'orange': 1, 'maroon': 2, 'red': 2, 'blue': 3, 'black': 4, 'purple': 5, 'green': 6, 'white': 7}\n",
            "RUN TRAIN\n",
            "100% 4/4 [00:00<00:00,  7.94it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/color directory\n",
            "Fail queries: []\n",
            "100% 4/4 [00:00<00:00,  9.22it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/color directory\n",
            "Fail queries: []\n",
            "RUN TEST\n",
            "100% 2/2 [00:00<00:00, 2981.03it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/color directory\n",
            "Fail queries: []\n",
            "100% 2/2 [00:00<00:00, 3761.71it/s]\n",
            "save result to /content/AIC2022-VER/data_sample/meta/srl/color directory\n",
            "Fail queries: []\n",
            "DONE\n",
            "Extracting SRL data [vehicle] ...\n",
            "{'sedan': 0, 'coupe': 0, 'mini-cooper': 0, 'prius': 0, 'suv': 1, 'wagon': 1, 'hatchback': 1, 'mpv': 1, 'minivan': 1, 'crossover': 1, 'van': 2, 'jeep': 3, 'pickup': 4, 'semi-trailer-truck': 5, 'cement-truck': 5, 'cargo-truck': 5, 'bus': 5, 'coach': 5, 'truck': 5, 'bus-truck': 5}\n",
            "RUN TRAIN\n",
            "100% 4/4 [00:00<00:00,  8.13it/s]\n",
            "Extract dataframe to /content/AIC2022-VER/data_sample/meta/srl/veh/train_fraction.csv\n",
            "Fail queries: []\n",
            "RUN TEST\n",
            "100% 2/2 [00:00<00:00, 3183.53it/s]\n",
            "Extract dataframe to /content/AIC2022-VER/data_sample/meta/srl/veh/veh_test_fraction.csv\n",
            "Fail queries: []\n",
            "100% 2/2 [00:00<00:00, 3664.75it/s]\n",
            "Extract dataframe to /content/AIC2022-VER/data_sample/meta/srl/veh/veh_test.csv\n",
            "Fail queries: []\n",
            "DONE\n",
            "Extracting SRL data [postproc] ...\n",
            "========== Parse result in train ==========\n",
            "100% 4/4 [00:00<00:00, 1867.46it/s]\n",
            "========== Parse result in test ==========\n",
            "100% 2/2 [00:00<00:00, 2154.79it/s]\n",
            "DONE\n",
            "CPU times: user 1.31 s, sys: 210 ms, total: 1.52 s\n",
            "Wall time: 2min 9s\n"
          ]
        }
      ],
      "source": [
        "%%time \n",
        "%cd $WORKDIR\n",
        "!./tools/preproc_srl.sh $DATADIR/meta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gpLncp3No_uD",
        "outputId": "d08ce06a-9662-4cde-d499-4fd663150bf4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/AIC2022-VER\n",
            "Extracting auxiliary tracks ...\n",
            "  0% 0/36 [00:00<?, ?it/s]Number of auxiliary tracks: 9\n",
            "100% 36/36 [00:03<00:00, 11.23it/s]\n",
            "  0% 0/23 [00:00<?, ?it/s]Number of auxiliary tracks: 6\n",
            "Number of auxiliary tracks: 4\n",
            "100% 23/23 [00:00<00:00, 191.85it/s]\n",
            "DONE\n",
            "Generate neighbor mapping ...\n",
            "100% 36/36 [00:00<00:00, 322.09it/s]\n",
            "100% 23/23 [00:00<00:00, 568.15it/s]\n",
            "DONE\n",
            "Refine neighbor ...\n",
            "  0% 0/2 [00:00<?, ?it/s]/content/AIC2022-VER/external/relation/relation_utils.py:65: RuntimeWarning: invalid value encountered in true_divide\n",
            "  return np.dot(vect_a, vect_b)/(np.linalg.norm(vect_a)*np.linalg.norm(vect_b)) #default: L2 norm\n",
            "100% 2/2 [00:00<00:00, 39.60it/s]\n",
            "DONE\n"
          ]
        }
      ],
      "source": [
        "%cd $WORKDIR\n",
        "!./tools/preproc_relation.sh $DATADIR/ $DATADIR/meta/test_tracks.json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWTff8D4dSte"
      },
      "source": [
        "## Predict meta data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "0kIhsdGjoEd9"
      },
      "outputs": [],
      "source": [
        "import os.path as osp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BtrBErfarTg8",
        "outputId": "bb536e05-a8c4-42e7-91ad-1386dd4988c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/AIC2022-VER\n",
            "saving to folder ./results/classification/neighbors/\n",
            "saving to ./results/classification/neighbors//color_prediction.json\n",
            "saving to ./results/classification/neighbors//vehicle_prediction.json\n",
            "Overriding configurating\n",
            "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b0-355c32eb.pth\n",
            "100% 20.4M/20.4M [00:00<00:00, 34.8MB/s]\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/utilities.py:94: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
            "  category=PossibleUserWarning,\n",
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "Missing logger folder: /content/AIC2022-VER/lightning_logs\n",
            "Predicting DataLoader 0: 100% 5/5 [00:03<00:00,  1.50it/s]\n",
            "Saved file to ./results/classification/neighbors//color_prediction.json\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/utilities.py:94: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
            "  category=PossibleUserWarning,\n",
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "Predicting DataLoader 0: 100% 5/5 [00:03<00:00,  1.58it/s]\n",
            "Saved file to ./results/classification/neighbors//vehicle_prediction.json\n"
          ]
        }
      ],
      "source": [
        "%cd $WORKDIR\n",
        "\n",
        "track_json_path = osp.join(DATADIR,'meta/relation/neighbor_tracks.json')\n",
        "!./tools/predict_srl.sh     configs/srl/col_infer.yml \\\n",
        "                            artifacts/color/model.ckpt \\\n",
        "                            configs/srl/veh_infer.yml \\\n",
        "                            artifacts/vehicle/model.ckpt \\\n",
        "                            $track_json_path \\\n",
        "                            $DATADIR/meta/extracted_frames \\\n",
        "                            ./results/classification/neighbors/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N28ARgo7cTRa",
        "outputId": "2b87ec1c-07dd-4574-f3a7-5f8ce83e0d7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/AIC2022-VER\n",
            "saving to folder ./results/classification/test_tracks/\n",
            "saving to ./results/classification/test_tracks//color_prediction.json\n",
            "saving to ./results/classification/test_tracks//vehicle_prediction.json\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/utilities.py:94: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
            "  category=PossibleUserWarning,\n",
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "Predicting DataLoader 0: 100% 1/1 [00:00<00:00,  2.67it/s]\n",
            "Saved file to ./results/classification/test_tracks//color_prediction.json\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "Loaded pretrained weights for efficientnet-b0\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/utilities.py:94: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
            "  category=PossibleUserWarning,\n",
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "Predicting DataLoader 0: 100% 1/1 [00:00<00:00,  2.45it/s]\n",
            "Saved file to ./results/classification/test_tracks//vehicle_prediction.json\n"
          ]
        }
      ],
      "source": [
        "%cd $WORKDIR\n",
        "\n",
        "track_json_path = osp.join(DATADIR,'meta/test_tracks.json')\n",
        "!./tools/predict_srl.sh     configs/srl/col_infer.yml \\\n",
        "                            artifacts/color/model.ckpt \\\n",
        "                            configs/srl/veh_infer.yml \\\n",
        "                            artifacts/vehicle/model.ckpt \\\n",
        "                            $track_json_path \\\n",
        "                            $DATADIR/meta/extracted_frames \\\n",
        "                            ./results/classification/test_tracks/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXy0wqkkdk74",
        "outputId": "1bd7b9ed-6376-47d4-cdda-c59182758ecd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/AIC2022-VER\n",
            "100% 2/2 [00:00<00:00, 269.63it/s]\n",
            "Number of track which turns either left:  0\n",
            "Number of track which turns either right:  0\n",
            "Number of track which stops:  0\n",
            "Number of straight-view track:  0\n"
          ]
        }
      ],
      "source": [
        "%cd $WORKDIR\n",
        "!python scripts/action/stop_turn_det.py -g $DATADIR/meta/test_tracks.json \\\n",
        "                                        -o results/classification/test_tracks/action_prediction.json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pw1i8ZFrixNw"
      },
      "source": [
        "## Predict retrieval candidates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "nw9zLdqDrTSM"
      },
      "outputs": [],
      "source": [
        "import os \n",
        "import torch\n",
        "from opt import Opts\n",
        "import os.path as osp \n",
        "from src.predictor import Predictor\n",
        "from src.models import MODEL_REGISTRY\n",
        "import gc\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "A7aw2FVmeVZ1"
      },
      "outputs": [],
      "source": [
        "config_data = {\n",
        "    'model-55pfbgaf:v15': {\n",
        "        'params': [\n",
        "            'extractors.img_encoder.args.version=3',\n",
        "            'data.track.args.image_size=288',\n",
        "            f\"data.text.args.json_path={osp.join(DATADIR,'meta/test_queries.json')}\",\n",
        "        ],\n",
        "        'version': 'v1',\n",
        "        'name': 'UTS-Best-0904'\n",
        "    },\n",
        "    'model-3kto46es:v7': {\n",
        "        'params': [\n",
        "            'extractors.img_encoder.args.version=3',\n",
        "            'data.track.args.image_size=224',\n",
        "            f\"data.text.args.json_path={osp.join(DATADIR,'meta/test_queries_nlpaug_v2.jso')}n\"\n",
        "        ],\n",
        "        'version': 'v2',\n",
        "        'name': 'hcmussbj_eff3'\n",
        "    },\n",
        "    'model-3r4fsrlp:v29': {\n",
        "        'params': [\n",
        "            'extractors.img_encoder.args.version=3',\n",
        "            'data.track.args.image_size=288',\n",
        "            f\"data.text.args.json_path={osp.join(DATADIR,'meta/test_queries.json')}\",\n",
        "        ],\n",
        "        'version': 'v1',\n",
        "        'name': 'UTS-Best-Second'\n",
        "    },\n",
        "    'model-5h72x53e:v18': {\n",
        "        'params': [\n",
        "            'extractors.img_encoder.args.version=3',\n",
        "            'data.track.args.image_size=256',\n",
        "            f\"data.text.args.json_path={osp.join(DATADIR,'meta/test_queries_nlpaug_v2.json')}\",\n",
        "        ],\n",
        "        'version': 'v2',\n",
        "        'name': 'hcmussbj_eff3_best'\n",
        "    },\n",
        "    \"model-2xjz0ux8:v25\": {\n",
        "        'params': [\n",
        "            'extractors.img_encoder.args.version=3',\n",
        "            'data.track.args.image_size=256',\n",
        "            f\"data.text.args.json_path={osp.join(DATADIR,'meta/test_queries_nlpaug_v2.json')}\",\n",
        "        ],\n",
        "        'version': 'v2',\n",
        "        'name': 'hcmussbj_eff3'\n",
        "    },\n",
        "    'model-3e6jnmrm:v16': {\n",
        "        'params': [\n",
        "            'extractors.img_encoder.args.version=3',\n",
        "            'data.track.args.image_size=288',\n",
        "            f\"data.text.args.json_path={osp.join(DATADIR,'meta/test_queries.json')}\",\n",
        "        ],\n",
        "        'version': 'v1',\n",
        "        'name': 'uts_eff3'\n",
        "    },\n",
        "    'model-1ityga7d:v20': {\n",
        "        'params': [\n",
        "            'extractors.img_encoder.args.version=3',\n",
        "            'data.track.args.image_size=288',\n",
        "            f\"data.text.args.json_path={osp.join(DATADIR,'meta/test_queries.json')}\",\n",
        "        ],\n",
        "        'version': 'v1',\n",
        "        'name': 'uts_eff3'\n",
        "    },\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "pkl7uK7SfPRO"
      },
      "outputs": [],
      "source": [
        "model_names = [\n",
        "    'model-55pfbgaf:v15',\n",
        "    'model-3kto46es:v7',\n",
        "    'model-3r4fsrlp:v29',\n",
        "    'model-5h72x53e:v18',\n",
        "    \"model-2xjz0ux8:v25\",\n",
        "    'model-3e6jnmrm:v16',\n",
        "    'model-1ityga7d:v20',\n",
        "]\n",
        "data_params = [ f\"data.track.args.json_path={osp.join(DATADIR,'meta/test_tracks.json')}\",\n",
        "                f\"data.track.args.image_dir={osp.join(DATADIR,'meta/extracted_frames')}\",\n",
        "                f\"data.text.args.shuffle=False\",\n",
        "                f\"data.track.args.motion_path={osp.join(DATADIR,'meta/motion_map')}\"\n",
        "            ] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "TfesCIk2fAw-"
      },
      "outputs": [],
      "source": [
        "def run_predict(cfg, model, mode, bs, savedir, top_k=184, NUM_TTA=1):\n",
        "    NUM_TTA = max(NUM_TTA, 1)\n",
        "    for tta_id in range(NUM_TTA):\n",
        "        savedir_tta = osp.join(savedir, str(tta_id))\n",
        "        os.makedirs(savedir_tta, exist_ok=True)\n",
        "        infer = Predictor(cfg=cfg, model=model, mode=mode, batch_size=bs, savedir=savedir_tta, top_k=top_k) # TOTAL NUMBER OF TRACKS (CANNOT SET -1)\n",
        "        infer.predict()\n",
        "        del infer\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
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        "colab": {
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        },
        "id": "X-SfRPQcfJZK",
        "outputId": "865b3804-d686-4e55-e0e2-49720574c016"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            " === running model-55pfbgaf:v15 1/7=== \n",
            "\n",
            "Overriding configurating\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b3-5fb5a3c3.pth\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "546d3f6d79bf48cd9ae7a2bfd421a0ce",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/47.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/utilities.py:94: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
            "  category=PossibleUserWarning,\n",
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using CPU to retrieve\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdecf82a39ad499a870cb8c39c471da9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "946a5361e9294b6482dc4b7c75241b89",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved query_embeds.json to /content/AIC2022-VER/results/retrieval/model-55pfbgaf:v15/0\n",
            "Saved track_embeds.json to /content/AIC2022-VER/results/retrieval/model-55pfbgaf:v15/0\n",
            "Save query results to  /content/AIC2022-VER/results/retrieval/model-55pfbgaf:v15/0/retrieval_results.json\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            " === running model-3kto46es:v7 2/7=== \n",
            "\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using CPU to retrieve\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f01eda4053a4c67ab11cd09921cba33",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a293ceeff094670988c48f018e37826",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved query_embeds.json to /content/AIC2022-VER/results/retrieval/model-3kto46es:v7/0\n",
            "Saved track_embeds.json to /content/AIC2022-VER/results/retrieval/model-3kto46es:v7/0\n",
            "Save query results to  /content/AIC2022-VER/results/retrieval/model-3kto46es:v7/0/retrieval_results.json\n",
            "\n",
            " === running model-3r4fsrlp:v29 3/7=== \n",
            "\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using CPU to retrieve\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d9ecf1302cb4f6180e76f715948f9a3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4330145609e54978bbc0f5fb26926319",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved query_embeds.json to /content/AIC2022-VER/results/retrieval/model-3r4fsrlp:v29/0\n",
            "Saved track_embeds.json to /content/AIC2022-VER/results/retrieval/model-3r4fsrlp:v29/0\n",
            "Save query results to  /content/AIC2022-VER/results/retrieval/model-3r4fsrlp:v29/0/retrieval_results.json\n",
            "\n",
            " === running model-5h72x53e:v18 4/7=== \n",
            "\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using CPU to retrieve\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "408ec1614b664c50be66b30082401999",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a337d6484714d9babba53683d71b48b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved query_embeds.json to /content/AIC2022-VER/results/retrieval/model-5h72x53e:v18/0\n",
            "Saved track_embeds.json to /content/AIC2022-VER/results/retrieval/model-5h72x53e:v18/0\n",
            "Save query results to  /content/AIC2022-VER/results/retrieval/model-5h72x53e:v18/0/retrieval_results.json\n",
            "\n",
            " === running model-2xjz0ux8:v25 5/7=== \n",
            "\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using CPU to retrieve\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cbd03b75cdb14b6eba6a82c010a2a411",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f472d6488a0d45f8926847804aa4fef1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved query_embeds.json to /content/AIC2022-VER/results/retrieval/model-2xjz0ux8:v25/0\n",
            "Saved track_embeds.json to /content/AIC2022-VER/results/retrieval/model-2xjz0ux8:v25/0\n",
            "Save query results to  /content/AIC2022-VER/results/retrieval/model-2xjz0ux8:v25/0/retrieval_results.json\n",
            "\n",
            " === running model-3e6jnmrm:v16 6/7=== \n",
            "\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using CPU to retrieve\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90e494ca31d544e2be6eddf06cc664f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e79b2a38491d47c99ebb1cb2fd8955e4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved query_embeds.json to /content/AIC2022-VER/results/retrieval/model-3e6jnmrm:v16/0\n",
            "Saved track_embeds.json to /content/AIC2022-VER/results/retrieval/model-3e6jnmrm:v16/0\n",
            "Save query results to  /content/AIC2022-VER/results/retrieval/model-3e6jnmrm:v16/0/retrieval_results.json\n",
            "\n",
            " === running model-1ityga7d:v20 7/7=== \n",
            "\n",
            "Overriding configurating\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n",
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "GPU available: False, used: False\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using CPU to retrieve\n"
          ]
        },
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              "Predicting: 0it [00:00, ?it/s]"
            ]
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              "Predicting: 0it [00:00, ?it/s]"
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          },
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved query_embeds.json to /content/AIC2022-VER/results/retrieval/model-1ityga7d:v20/0\n",
            "Saved track_embeds.json to /content/AIC2022-VER/results/retrieval/model-1ityga7d:v20/0\n",
            "Save query results to  /content/AIC2022-VER/results/retrieval/model-1ityga7d:v20/0/retrieval_results.json\n"
          ]
        }
      ],
      "source": [
        "%cd $WORKDIR\n",
        "\n",
        "for i, model_name in enumerate(model_names):\n",
        "    print(f'\\n === running {model_name} {i+1}/{len(model_names)}=== \\n')\n",
        "    params = ['-o'] + config_data[model_name]['params'] + data_params\n",
        "    savedir = osp.join(WORKDIR, f\"results/retrieval/{model_name}\")\n",
        "    resume_ckpt = osp.join(WORKDIR, f\"artifacts/{model_name}/model.ckpt\")\n",
        "    cfg_path = osp.join(WORKDIR,\"configs/inference.yml\") if config_data[model_name]['version'] == 'v1' else osp.join(WORKDIR,\"configs/inferencev2.yml\")\n",
        "    cfg = Opts(cfg=cfg_path).parse_args(params)\n",
        "    model = MODEL_REGISTRY.get(cfg.model[\"name\"])(cfg)\n",
        "    model = model.load_from_checkpoint(resume_ckpt, config=cfg, strict=False)\n",
        "    run_predict(cfg, model, \"complex\", 32, savedir, top_k=184)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_UHOqkmzac9",
        "outputId": "bd61408f-2d23-434b-86a7-239276f30c7a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "saved prediction file at: /content/AIC2022-VER/results/retrieval/ensemble.json\n"
          ]
        }
      ],
      "source": [
        "save_dir = osp.join(WORKDIR, 'results/retrieval')\n",
        "results = {}\n",
        "for m in model_names:\n",
        "    for idx in range(1): # TTA_IDS = [0]\n",
        "      pred_json = osp.join(save_dir, m, str(idx), 'retrieval_results.json')      \n",
        "      with open(pred_json, 'r') as f:\n",
        "          data = json.load(f)\n",
        "      for q, preds in data.items():\n",
        "          if not(q in results.keys()):\n",
        "              results[q] = {}\n",
        "              results[q] = {}\n",
        "          tmp = {}\n",
        "          for i, k in enumerate(data[q]['pred_ids']):\n",
        "              tmp[k] = data[q]['scores'][i]\n",
        "          results[q][m] = tmp\n",
        "\n",
        "# print(results.keys())\n",
        "# print(data['1ed5b63a-0840-4fc3-8150-dd73b9b809ce']['pred_ids'])\n",
        "\n",
        "q_ids = results.keys()\n",
        "results_e = {}\n",
        "for q in q_ids:\n",
        "    results_e[q] = {}\n",
        "    for model_name, model_res in results[q].items():\n",
        "        for q_i in model_res.keys():\n",
        "            if q_i in results_e[q]:\n",
        "                results_e[q][q_i].append(1 * model_res[q_i])\n",
        "            else:\n",
        "                results_e[q][q_i] = [1 * model_res[q_i]]\n",
        "# print(results_e['1ed5b63a-0840-4fc3-8150-dd73b9b809ce'])\n",
        "\n",
        "for q in q_ids:\n",
        "    for k in results_e[q]:\n",
        "        results_e[q][k] = sum(results_e[q][k])\n",
        "# print(results_e['1ed5b63a-0840-4fc3-8150-dd73b9b809ce'])\n",
        "\n",
        "result_final = {}\n",
        "for q in q_ids:\n",
        "    result_final[q] = [(k, v) for k, v in results_e[q].items()]\n",
        "    result_final[q] = sorted(result_final[q], key=lambda tup: tup[1], reverse=True)\n",
        "    result_final[q] = [x[0] for x in result_final[q]]\n",
        "\n",
        "# print(result_final['1ed5b63a-0840-4fc3-8150-dd73b9b809ce'])\n",
        "result_path = osp.join(save_dir,'ensemble.json')\n",
        "with open(result_path, 'w') as f:\n",
        "    json.dump(result_final, f, indent=4)\n",
        "print(f'saved prediction file at: {result_path}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQ9n6GLbg_Hd"
      },
      "source": [
        "### Sanity check (only for real test data, have 184 queries)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24VYUW9t_rLp",
        "outputId": "5e540d2a-c329-48f1-9a5a-702a2bb9cdfa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n",
            "2\n",
            "2\n",
            "['c19db37b-ec12-43ca-902e-5ff2d8ef3b5a', 'a1186b5d-6be7-464a-aa79-bad9acdf2895']\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "def load(filepath):\n",
        "    with open(filepath, 'r') as f:\n",
        "        data = json.load(f)\n",
        "    return data\n",
        "sub = load(result_path)\n",
        "print(len(sub.keys()))\n",
        "for k in sub.keys():\n",
        "  if len(set(sub[k])) != 184:\n",
        "      print(len(set(sub[k])))\n",
        "print(sub[k])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7TKV6R4ePsW"
      },
      "source": [
        "## Refine results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EB6LECI7SiXf",
        "outputId": "cdd26e69-c85a-41d2-80c4-616d17347d04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "parse relation result and save to /content/AIC2022-VER/results/test_relation_action_f1\n",
            "\r  0% 0/2 [00:00<?, ?it/s]\r100% 2/2 [00:00<00:00, 912.60it/s]\n",
            "run refinement, save result to /content/AIC2022-VER/results/\n",
            "100% 2/2 [00:00<00:00, 1069.43it/s]\n",
            "n svo: 0\n",
            "saved full class submission to: /content/AIC2022-VER/results/ensemble_full_Class.json\n",
            "saved action only submission to: /content/AIC2022-VER/results/ensemble_action.json\n",
            "saved vehcol only submission to: /content/AIC2022-VER/results/ensemble_vehcol.json\n"
          ]
        }
      ],
      "source": [
        "!./tools/refine.sh  $WORKDIR/results/retrieval/ensemble.json \\\n",
        "                    $DATADIR/meta/srl/postproc/test_srl.csv \\\n",
        "                    $WORKDIR/results/test_relation_action_f1 \\\n",
        "                    $DATADIR/meta/relation/test_tracks_relation.json \\\n",
        "                    $DATADIR/meta/relation/neighbor_tracks.json \\\n",
        "                    $WORKDIR/results/classification/ \\\n",
        "                    $WORKDIR/results/ "
      ]
    }
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