MDETR_DIME.ipynb

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            "_model_module_version": "1.5.0",
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            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
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      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "Cython.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XYogVbdUWL8_",
        "outputId": "4a180459-5e2c-47dc-a663-74b42b81a0ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.29.27'"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import Cython"
      ],
      "metadata": {
        "id": "LLDvUICVnpPn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Preparation"
      ],
      "metadata": {
        "id": "wDvA5TYGoPwg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zeLvGj4IrTd",
        "outputId": "013e8fd6-d0da-4dba-d4ef-2a2b45c37d7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'mdetr'...\n",
            "remote: Enumerating objects: 189, done.\u001b[K\n",
            "remote: Counting objects: 100% (189/189), done.\u001b[K\n",
            "remote: Compressing objects: 100% (149/149), done.\u001b[K\n",
            "remote: Total 189 (delta 69), reused 146 (delta 39), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (189/189), 9.64 MiB | 21.75 MiB/s, done.\n",
            "Resolving deltas: 100% (69/69), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ashkamath/mdetr.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.insert(0,'/content/mdetr')"
      ],
      "metadata": {
        "id": "WMnQqb-9T2fE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd mdetr/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwkK8u5FQsFY",
        "outputId": "ddb2d365-a536-45bb-aee2-b7536ce3b9ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mdetr\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile requirements.txt\n",
        "torch==1.7.0\n",
        "torchvision>=0.6.0\n",
        "cython\n",
        "scipy\n",
        "xmltodict\n",
        "tqdm\n",
        "transformers==4.5.1\n",
        "onnx\n",
        "onnxruntime\n",
        "prettytable\n",
        "submitit\n",
        "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI&egg=pycocotools\n",
        "git+https://github.com/cocodataset/panopticapi.git#egg=panopticapi\n",
        "timm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LRA2zIeK2XF7",
        "outputId": "aeb36ebb-8307-4bd8-8ff1-b60fc474d42e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting requirements.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Vvh95-nEQqRw",
        "outputId": "39c51656-c191-4e80-ca78-16a9da229828"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pycocotools\n",
            "  Cloning https://github.com/cocodataset/cocoapi.git to /tmp/pip-install-z1yerfiu/pycocotools_5b6899fd4f304e22babcfe54955d415e\n",
            "  Running command git clone -q https://github.com/cocodataset/cocoapi.git /tmp/pip-install-z1yerfiu/pycocotools_5b6899fd4f304e22babcfe54955d415e\n",
            "Collecting panopticapi\n",
            "  Cloning https://github.com/cocodataset/panopticapi.git to /tmp/pip-install-z1yerfiu/panopticapi_e05138e7c5bf4edaa482ece639f239b2\n",
            "  Running command git clone -q https://github.com/cocodataset/panopticapi.git /tmp/pip-install-z1yerfiu/panopticapi_e05138e7c5bf4edaa482ece639f239b2\n",
            "Collecting torch==1.7.0\n",
            "  Downloading torch-1.7.0-cp37-cp37m-manylinux1_x86_64.whl (776.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 776.7 MB 4.9 kB/s \n",
            "\u001b[?25hRequirement already satisfied: torchvision>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (0.11.1+cu111)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (0.29.27)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (1.4.1)\n",
            "Collecting xmltodict\n",
            "  Downloading xmltodict-0.12.0-py2.py3-none-any.whl (9.2 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (4.62.3)\n",
            "Collecting transformers==4.5.1\n",
            "  Downloading transformers-4.5.1-py3-none-any.whl (2.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1 MB 32.5 MB/s \n",
            "\u001b[?25hCollecting onnx\n",
            "  Downloading onnx-1.10.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (12.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 12.7 MB 36.5 MB/s \n",
            "\u001b[?25hCollecting onnxruntime\n",
            "  Downloading onnxruntime-1.10.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 31.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: prettytable in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 10)) (3.0.0)\n",
            "Collecting submitit\n",
            "  Downloading submitit-1.4.1-py3-none-any.whl (70 kB)\n",
            "\u001b[K     |████████████████████████████████| 70 kB 8.3 MB/s \n",
            "\u001b[?25hCollecting timm\n",
            "  Downloading timm-0.5.4-py3-none-any.whl (431 kB)\n",
            "\u001b[K     |████████████████████████████████| 431 kB 42.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools->-r requirements.txt (line 12)) (57.4.0)\n",
            "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools->-r requirements.txt (line 12)) (3.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from panopticapi->-r requirements.txt (line 13)) (1.19.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from panopticapi->-r requirements.txt (line 13)) (7.1.2)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from torch==1.7.0->-r requirements.txt (line 1)) (0.16.0)\n",
            "Collecting dataclasses\n",
            "  Downloading dataclasses-0.6-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.7.0->-r requirements.txt (line 1)) (3.10.0.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.5.1->-r requirements.txt (line 7)) (3.4.2)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 12.9 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 38.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.5.1->-r requirements.txt (line 7)) (4.10.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==4.5.1->-r requirements.txt (line 7)) (21.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.5.1->-r requirements.txt (line 7)) (2019.12.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.5.1->-r requirements.txt (line 7)) (2.23.0)\n",
            "Collecting torchvision>=0.6.0\n",
            "  Downloading torchvision-0.11.3-cp37-cp37m-manylinux1_x86_64.whl (23.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 23.2 MB 1.8 MB/s \n",
            "\u001b[?25h  Downloading torchvision-0.11.2-cp37-cp37m-manylinux1_x86_64.whl (23.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 23.3 MB 1.6 MB/s \n",
            "\u001b[?25h  Downloading torchvision-0.11.1-cp37-cp37m-manylinux1_x86_64.whl (23.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 23.3 MB 1.6 MB/s \n",
            "\u001b[?25h  Downloading torchvision-0.10.1-cp37-cp37m-manylinux1_x86_64.whl (22.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 22.1 MB 1.7 MB/s \n",
            "\u001b[?25h  Downloading torchvision-0.10.0-cp37-cp37m-manylinux1_x86_64.whl (22.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 22.1 MB 49.4 MB/s \n",
            "\u001b[?25h  Downloading torchvision-0.9.1-cp37-cp37m-manylinux1_x86_64.whl (17.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 17.4 MB 196 kB/s \n",
            "\u001b[?25h  Downloading torchvision-0.9.0-cp37-cp37m-manylinux1_x86_64.whl (17.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 17.3 MB 119 kB/s \n",
            "\u001b[?25h  Downloading torchvision-0.8.2-cp37-cp37m-manylinux1_x86_64.whl (12.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 12.8 MB 29.3 MB/s \n",
            "\u001b[?25h  Downloading torchvision-0.8.1-cp37-cp37m-manylinux1_x86_64.whl (12.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 12.7 MB 10.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools->-r requirements.txt (line 12)) (1.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools->-r requirements.txt (line 12)) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools->-r requirements.txt (line 12)) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools->-r requirements.txt (line 12)) (3.0.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.1.0->pycocotools->-r requirements.txt (line 12)) (1.15.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx->-r requirements.txt (line 8)) (3.17.3)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime->-r requirements.txt (line 9)) (2.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prettytable->-r requirements.txt (line 10)) (0.2.5)\n",
            "Requirement already satisfied: cloudpickle>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from submitit->-r requirements.txt (line 11)) (1.3.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.5.1->-r requirements.txt (line 7)) (3.7.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.5.1->-r requirements.txt (line 7)) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.5.1->-r requirements.txt (line 7)) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.5.1->-r requirements.txt (line 7)) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.5.1->-r requirements.txt (line 7)) (3.0.4)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.5.1->-r requirements.txt (line 7)) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.5.1->-r requirements.txt (line 7)) (1.1.0)\n",
            "Building wheels for collected packages: pycocotools, panopticapi\n",
            "  Building wheel for pycocotools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycocotools: filename=pycocotools-2.0-cp37-cp37m-linux_x86_64.whl size=263965 sha256=4aeca4af0cf1e320ffcb1fb72a91f4c2e305f68c210ec65203ca2cee9baa6972\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-v8jusns4/wheels/e2/6b/1d/344ac773c7495ea0b85eb228bc66daec7400a143a92d36b7b1\n",
            "  Building wheel for panopticapi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for panopticapi: filename=panopticapi-0.1-py3-none-any.whl size=8306 sha256=cbe7d39dcd379ec1d77b350d8c2b6ed8d685159db26fdcf1f4c7169dca03e681\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-v8jusns4/wheels/ad/89/b8/b66cce9246af3d71d65d72c85ab993fd28e7578e1b0ed197f1\n",
            "Successfully built pycocotools panopticapi\n",
            "Installing collected packages: dataclasses, torch, torchvision, tokenizers, sacremoses, xmltodict, transformers, timm, submitit, pycocotools, panopticapi, onnxruntime, onnx\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.10.0+cu111\n",
            "    Uninstalling torch-1.10.0+cu111:\n",
            "      Successfully uninstalled torch-1.10.0+cu111\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.11.1+cu111\n",
            "    Uninstalling torchvision-0.11.1+cu111:\n",
            "      Successfully uninstalled torchvision-0.11.1+cu111\n",
            "  Attempting uninstall: pycocotools\n",
            "    Found existing installation: pycocotools 2.0.4\n",
            "    Uninstalling pycocotools-2.0.4:\n",
            "      Successfully uninstalled pycocotools-2.0.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.7.0 which is incompatible.\n",
            "torchaudio 0.10.0+cu111 requires torch==1.10.0, but you have torch 1.7.0 which is incompatible.\u001b[0m\n",
            "Successfully installed dataclasses-0.6 onnx-1.10.2 onnxruntime-1.10.0 panopticapi-0.1 pycocotools-2.0 sacremoses-0.0.47 submitit-1.4.1 timm-0.5.4 tokenizers-0.10.3 torch-1.7.0 torchvision-0.8.1 transformers-4.5.1 xmltodict-0.12.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "dataclasses"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install lime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OcVp_X4z37C",
        "outputId": "f43307e2-c3f4-4957-87f5-b2e6dcc1d1de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lime\n",
            "  Downloading lime-0.2.0.1.tar.gz (275 kB)\n",
            "\u001b[K     |████████████████████████████████| 275 kB 5.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from lime) (3.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lime) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from lime) (1.4.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from lime) (4.62.3)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from lime) (1.0.2)\n",
            "Requirement already satisfied: scikit-image>=0.12 in /usr/local/lib/python3.7/dist-packages (from lime) (0.18.3)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (2.4.1)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (7.1.2)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (1.2.0)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (2021.11.2)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (2.6.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (3.0.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->lime) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->lime) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->lime) (3.1.0)\n",
            "Building wheels for collected packages: lime\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283857 sha256=ef63e4343cf2a2c0b61bf68f86d2c91f19ccdfce4b5d3344865a262481edb2cf\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/cb/e5/ac701e12d365a08917bf4c6171c0961bc880a8181359c66aa7\n",
            "Successfully built lime\n",
            "Installing collected packages: lime\n",
            "Successfully installed lime-0.2.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir datasets/raw"
      ],
      "metadata": {
        "id": "PqaH3Jm4uY9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd datasets/raw"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNKQlurrukVK",
        "outputId": "fcc10f16-cda0-49d6-e41b-8d4b709f82cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mdetr/datasets/raw\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "print(torch.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncyBfR7Wetju",
        "outputId": "8233bcc0-1205-450a-8105-d022038b6b5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqZVmII6urkS",
        "outputId": "b7f20c8c-6620-40ce-a746-f44f54bb432c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-02-08 03:26:32--  https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 104.22.74.142, 172.67.9.4, 104.22.75.142, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.74.142|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 19021600724 (18G) [application/zip]\n",
            "Saving to: ‘CLEVR_v1.0.zip’\n",
            "\n",
            "CLEVR_v1.0.zip      100%[===================>]  17.71G  30.3MB/s    in 8m 5s   \n",
            "\n",
            "2022-02-08 03:34:38 (37.4 MB/s) - ‘CLEVR_v1.0.zip’ saved [19021600724/19021600724]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://zenodo.org/record/4721981/files/clevr_annotations.zip?download=1\n"
      ],
      "metadata": {
        "id": "OVLnErRSwsDY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5517a87d-0caa-4e13-ef63-64b30b621927"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-02-08 03:34:38--  https://zenodo.org/record/4721981/files/clevr_annotations.zip?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
            "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 310336276 (296M) [application/octet-stream]\n",
            "Saving to: ‘clevr_annotations.zip?download=1’\n",
            "\n",
            "clevr_annotations.z 100%[===================>] 295.96M  5.47MB/s    in 28s     \n",
            "\n",
            "2022-02-08 03:35:08 (10.6 MB/s) - ‘clevr_annotations.zip?download=1’ saved [310336276/310336276]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip CLEVR_v1.0.zip"
      ],
      "metadata": {
        "id": "6AP6Tesvxtqn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7eb21537-4350-46cd-a5b0-bb38fd4ab290"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: CLEVR_v1.0/images/train/CLEVR_train_011644.png  \n",
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            "  inflating: CLEVR_v1.0/images/train/CLEVR_train_025084.png  \n",
            "  inflating: CLEVR_v1.0/images/train/CLEVR_train_003429.png  \n",
            "CLEVR_v1.0/images/train/CLEVR_train_003429.png:  write error (disk full?).  Continue? (y/n/^C) "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm CLEVR_v1.0.zip"
      ],
      "metadata": {
        "id": "9yxqR7EuxzAN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip clevr_annotations.zip\\?download\\=1"
      ],
      "metadata": {
        "id": "JuN1AOOfz5Y4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71e9b3ed-10b8-41b6-a23d-a39f5368d012"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  clevr_annotations.zip?download=1\n",
            "   creating: clevr_annotations/\n",
            "   creating: clevr_annotations/full/\n",
            "  inflating: clevr_annotations/full/train.json  \n",
            "  inflating: clevr_annotations/full/val.json  \n",
            "   creating: clevr_annotations/cogent_full/\n",
            "  inflating: clevr_annotations/cogent_full/train.json  \n",
            "  inflating: clevr_annotations/cogent_full/val.json  \n",
            "  inflating: clevr_annotations/cogent_full/valB.json  \n",
            "   creating: clevr_annotations/medium/\n",
            "  inflating: clevr_annotations/medium/train.json  \n",
            "  inflating: clevr_annotations/medium/val.json  \n",
            "   creating: clevr_annotations/clevrref/\n",
            "  inflating: clevr_annotations/clevrref/train.json  \n",
            "  inflating: clevr_annotations/clevrref/val.json  \n",
            "   creating: clevr_annotations/cogent_medium/\n",
            "  inflating: clevr_annotations/cogent_medium/train.json  \n",
            "  inflating: clevr_annotations/cogent_medium/val.json  \n",
            "  inflating: clevr_annotations/cogent_medium/valB.json  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ../.."
      ],
      "metadata": {
        "id": "EDZkpdXliBmi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "793d6984-4e72-4206-df3d-9105cbb961a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mdetr\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(\"configs/clevr.json\", \"r\") as f:\n",
        "  data = json.load(f)\n",
        "\n",
        "data[\"clevr_img_path\"] = \"datasets/raw/CLEVR_v1.0/images\"\n",
        "data[\"clevr_ann_path\"] = \"datasets/raw/clevr_annotations/full\"\n",
        "\n",
        "with open(\"configs/clevr.json\", \"w\") as f:\n",
        "    json.dump(data, f)\n"
      ],
      "metadata": {
        "id": "8oLxSs6vzXgb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%env CUBLAS_WORKSPACE_CONFIG=:16:8"
      ],
      "metadata": {
        "id": "rBQ_UZU6jVri",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91029edb-92f7-4de5-fe80-9064429753c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: CUBLAS_WORKSPACE_CONFIG=:16:8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Create Dataset"
      ],
      "metadata": {
        "id": "DXPOvIzooWjf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#in datasets/clevr.py\n",
        "import io\n",
        "import json\n",
        "import os\n",
        "import pickle\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.utils.data\n",
        "import torchvision\n",
        "from PIL import Image\n",
        "from transformers import RobertaTokenizerFast\n",
        "\n",
        "import datasets.transforms as T\n",
        "\n",
        "#from .coco import ConvertCocoPolysToMask, create_positive_map\n",
        "from datasets.coco import ConvertCocoPolysToMask, create_positive_map\n",
        "\n",
        "\n",
        "ALL_ATTRIBUTES = [\n",
        "    \"small\",\n",
        "    \"large\",\n",
        "    \"gray\",\n",
        "    \"red\",\n",
        "    \"blue\",\n",
        "    \"green\",\n",
        "    \"brown\",\n",
        "    \"purple\",\n",
        "    \"cyan\",\n",
        "    \"yellow\",\n",
        "    \"cube\",\n",
        "    \"sphere\",\n",
        "    \"cylinder\",\n",
        "    \"rubber\",\n",
        "    \"metal\",\n",
        "]\n",
        "\n",
        "\n",
        "def _encode_answer(target, answer):\n",
        "    if answer in [\"yes\", \"no\"]:\n",
        "        target[\"answer_type\"] = torch.as_tensor(0, dtype=torch.long)\n",
        "        target[\"answer_binary\"] = torch.as_tensor(0.0 if answer == \"no\" else 1.0)\n",
        "        target[\"answer_attr\"] = torch.as_tensor(-100, dtype=torch.long)\n",
        "        target[\"answer_reg\"] = torch.as_tensor(-100, dtype=torch.long)\n",
        "    elif answer in ALL_ATTRIBUTES:\n",
        "        target[\"answer_type\"] = torch.as_tensor(1, dtype=torch.long)\n",
        "        target[\"answer_binary\"] = torch.as_tensor(0.0)\n",
        "        target[\"answer_attr\"] = torch.as_tensor(ALL_ATTRIBUTES.index(answer), dtype=torch.long)\n",
        "        target[\"answer_reg\"] = torch.as_tensor(-100, dtype=torch.long)\n",
        "    else:\n",
        "        target[\"answer_type\"] = torch.as_tensor(2, dtype=torch.long)\n",
        "        target[\"answer_binary\"] = torch.as_tensor(0.0)\n",
        "        target[\"answer_attr\"] = torch.as_tensor(-100, dtype=torch.long)\n",
        "        target[\"answer_reg\"] = torch.as_tensor(int(answer), dtype=torch.long)\n",
        "    return target\n",
        "\n",
        "class ClevrDetection(torchvision.datasets.CocoDetection):\n",
        "    def __init__(self, img_folder, ann_file, transforms, return_masks, return_tokens, tokenizer, do_qa):\n",
        "        super(ClevrDetection, self).__init__(img_folder, ann_file)\n",
        "        self._transforms = transforms\n",
        "        self.prepare = ConvertCocoPolysToMask(return_masks, return_tokens)\n",
        "        self.tokenizer = tokenizer\n",
        "        self.return_tokens = return_tokens\n",
        "        self.do_qa = do_qa\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img, target = super(ClevrDetection, self).__getitem__(idx)\n",
        "        image_id = self.ids[idx]\n",
        "        img_data = self.coco.loadImgs(image_id)[0]\n",
        "        caption = img_data[\"caption\"] if \"caption\" in img_data else None\n",
        "        target = {\"image_id\": image_id, \"annotations\": target, \"caption\": caption}\n",
        "        img, target = self.prepare(img, target)\n",
        "        if self.do_qa:\n",
        "            answer = img_data[\"answer\"]\n",
        "            target = _encode_answer(target, answer)\n",
        "\n",
        "        if self.return_tokens:\n",
        "            assert len(target[\"boxes\"]) == len(target[\"tokens_positive\"])\n",
        "            tokenized = self.tokenizer(caption, return_tensors=\"pt\")\n",
        "            # construct a map such that positive_map[i,j] = True iff box i is associated to token j\n",
        "            target[\"positive_map\"] = create_positive_map(tokenized, target[\"tokens_positive\"])\n",
        "\n",
        "        if self._transforms is not None:\n",
        "            img, target = self._transforms(img, target)\n",
        "        return img, target\n",
        "\n",
        "def make_clevr_transforms(image_set, cautious=False):\n",
        "\n",
        "    normalize = T.Compose([T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
        "\n",
        "    scales = [256, 288, 320, 352, 384]\n",
        "\n",
        "    if image_set == \"train\":\n",
        "        horizontal = [] if cautious else [T.RandomHorizontalFlip()]\n",
        "        return T.Compose(\n",
        "            horizontal\n",
        "            + [\n",
        "                T.RandomSelect(\n",
        "                    T.RandomResize(scales, max_size=512),\n",
        "                    T.Compose(\n",
        "                        [\n",
        "                            T.RandomResize([320, 352, 384]),\n",
        "                            T.RandomSizeCrop(256, 512, respect_boxes=cautious),\n",
        "                            T.RandomResize(scales, max_size=512),\n",
        "                        ]\n",
        "                    ),\n",
        "                ),\n",
        "                normalize,\n",
        "            ]\n",
        "        )\n",
        "\n",
        "    if image_set == \"val\":\n",
        "        return T.Compose(\n",
        "            [\n",
        "                # T.RandomResize([480], max_size=1333),\n",
        "                normalize,\n",
        "            ]\n",
        "        )\n",
        "\n",
        "    raise ValueError(f\"unknown {image_set}\")        \n"
      ],
      "metadata": {
        "id": "5Hki4VRpxMSg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dset = build_dataset('clevr', image_set=\"val\", args=args)\n",
        "tokenizer = RobertaTokenizerFast.from_pretrained('distilroberta-base')\n",
        "\n",
        "img_dir = 'datasets/raw/CLEVR_v1.0/images/val'\n",
        "ann_file = 'datasets/raw/clevr_annotations/full/val.json'\n",
        "image_set = 'val'\n",
        "dataset = ClevrDetection(\n",
        "    img_dir,\n",
        "    ann_file,\n",
        "    transforms=make_clevr_transforms(image_set, cautious=True),\n",
        "    return_masks=False,\n",
        "    return_tokens=True,\n",
        "    tokenizer=tokenizer,\n",
        "    do_qa=1,\n",
        ")"
      ],
      "metadata": {
        "id": "5B37xRnk0v0-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150,
          "referenced_widgets": [
            "8bcd6fb761c54a6aaef900e7791796a2",
            "6e40ac647ec64e25a0a53f33d01a270a",
            "d539595f85784245ad15534521f6bdbf",
            "02b612f725d54c1ebd497e92e27339c8",
            "ba794de67e34425ba1d18e1614850e08",
            "72f9218da02b41608f89ab70bcda4c1a",
            "585fc49eb763441d9f56215f1a49b2cd",
            "2df57ddeb57940daa2a70041676c3367",
            "50f4838a9522422ab5642c8d56d6672f",
            "56cbfa89c6204810929e2378b6899eb9",
            "5b54fa68dd2046e0b6425b461ec1bc4f",
            "9ded995eece247dbad48bf3633eda15a",
            "c7b811700c154dc79a0692eafea4da80",
            "80259b54f3184b40846aa5096f63f3c8",
            "85b8ebbc59b1412d9e3f917d7c79c7ee",
            "b5b08ef1268244c5b3a6e43bef944729",
            "e69d95c1ba4545649d534792f2d88a09",
            "fdd09d8ceca14267b7eed0e442b091fa",
            "4afad8f126814f9499238f800086e7a9",
            "d991f938fdfa4ae49213301103af1b46",
            "6a2d8f2d671f43ef979972cea9acb73d",
            "99fd14cda7934708a8a9b732faa9e7c6"
          ]
        },
        "outputId": "16237f1e-a9a0-4ae0-d79d-429b216318c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8bcd6fb761c54a6aaef900e7791796a2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ded995eece247dbad48bf3633eda15a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=3.14s)\n",
            "creating index...\n",
            "index created!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Get MDETR model"
      ],
      "metadata": {
        "id": "9AM10ZI1oas5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#in main.py\n",
        "import argparse\n",
        "import datetime\n",
        "import json\n",
        "import os\n",
        "import random\n",
        "import time\n",
        "from collections import namedtuple\n",
        "from copy import deepcopy\n",
        "from functools import partial\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.utils\n",
        "from torch.utils.data import ConcatDataset, DataLoader, DistributedSampler\n",
        "\n",
        "import util.dist as dist\n",
        "import util.misc as utils\n",
        "from datasets import build_dataset, get_coco_api_from_dataset\n",
        "from datasets.clevrref import ClevrRefEvaluator\n",
        "from datasets.coco_eval import CocoEvaluator\n",
        "from datasets.flickr_eval import FlickrEvaluator\n",
        "from datasets.phrasecut_eval import PhrasecutEvaluator\n",
        "from datasets.refexp import RefExpEvaluator\n",
        "from engine import evaluate, train_one_epoch\n",
        "from models import build_model\n",
        "from models.postprocessors import build_postprocessors\n",
        "\n",
        "\n",
        "def get_args_parser():\n",
        "    parser = argparse.ArgumentParser(\"Set transformer detector\", add_help=False)\n",
        "    parser.add_argument(\"--run_name\", default=\"\", type=str)\n",
        "\n",
        "    # Dataset specific\n",
        "    parser.add_argument(\"--dataset_config\", default=None, required=True)\n",
        "    parser.add_argument(\"--do_qa\", action=\"store_true\", help=\"Whether to do question answering\")\n",
        "    parser.add_argument(\n",
        "        \"--predict_final\",\n",
        "        action=\"store_true\",\n",
        "        help=\"If true, will predict if a given box is in the actual referred set. Useful for CLEVR-Ref+ only currently.\",\n",
        "    )\n",
        "    parser.add_argument(\"--no_detection\", action=\"store_true\", help=\"Whether to train the detector\")\n",
        "    parser.add_argument(\n",
        "        \"--split_qa_heads\", action=\"store_true\", help=\"Whether to use a separate head per question type in vqa\"\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--combine_datasets\", nargs=\"+\", help=\"List of datasets to combine for training\", default=[\"flickr\"]\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--combine_datasets_val\", nargs=\"+\", help=\"List of datasets to combine for eval\", default=[\"flickr\"]\n",
        "    )\n",
        "\n",
        "    parser.add_argument(\"--coco_path\", type=str, default=\"\")\n",
        "    parser.add_argument(\"--vg_img_path\", type=str, default=\"\")\n",
        "    parser.add_argument(\"--vg_ann_path\", type=str, default=\"\")\n",
        "    parser.add_argument(\"--clevr_img_path\", type=str, default=\"\")\n",
        "    parser.add_argument(\"--clevr_ann_path\", type=str, default=\"\")\n",
        "    parser.add_argument(\"--phrasecut_ann_path\", type=str, default=\"\")\n",
        "    parser.add_argument(\n",
        "        \"--phrasecut_orig_ann_path\",\n",
        "        type=str,\n",
        "        default=\"\",\n",
        "    )\n",
        "    parser.add_argument(\"--modulated_lvis_ann_path\", type=str, default=\"\")\n",
        "\n",
        "    # Training hyper-parameters\n",
        "    parser.add_argument(\"--lr\", default=1e-4, type=float)\n",
        "    parser.add_argument(\"--lr_backbone\", default=1e-5, type=float)\n",
        "    parser.add_argument(\"--text_encoder_lr\", default=5e-5, type=float)\n",
        "    parser.add_argument(\"--batch_size\", default=2, type=int)\n",
        "    parser.add_argument(\"--weight_decay\", default=1e-4, type=float)\n",
        "    parser.add_argument(\"--epochs\", default=40, type=int)\n",
        "    parser.add_argument(\"--lr_drop\", default=35, type=int)\n",
        "    parser.add_argument(\n",
        "        \"--epoch_chunks\",\n",
        "        default=-1,\n",
        "        type=int,\n",
        "        help=\"If greater than 0, will split the training set into chunks and validate/checkpoint after each chunk\",\n",
        "    )\n",
        "    parser.add_argument(\"--optimizer\", default=\"adam\", type=str)\n",
        "    parser.add_argument(\"--clip_max_norm\", default=0.1, type=float, help=\"gradient clipping max norm\")\n",
        "    parser.add_argument(\n",
        "        \"--eval_skip\",\n",
        "        default=1,\n",
        "        type=int,\n",
        "        help='do evaluation every \"eval_skip\" frames',\n",
        "    )\n",
        "\n",
        "    parser.add_argument(\n",
        "        \"--schedule\",\n",
        "        default=\"linear_with_warmup\",\n",
        "        type=str,\n",
        "        choices=(\"step\", \"multistep\", \"linear_with_warmup\", \"all_linear_with_warmup\"),\n",
        "    )\n",
        "    parser.add_argument(\"--ema\", action=\"store_true\")\n",
        "    parser.add_argument(\"--ema_decay\", type=float, default=0.9998)\n",
        "    parser.add_argument(\"--fraction_warmup_steps\", default=0.01, type=float, help=\"Fraction of total number of steps\")\n",
        "\n",
        "    # Model parameters\n",
        "    parser.add_argument(\n",
        "        \"--frozen_weights\",\n",
        "        type=str,\n",
        "        default=None,\n",
        "        help=\"Path to the pretrained model. If set, only the mask head will be trained\",\n",
        "    )\n",
        "\n",
        "    parser.add_argument(\n",
        "        \"--freeze_text_encoder\", action=\"store_true\", help=\"Whether to freeze the weights of the text encoder\"\n",
        "    )\n",
        "\n",
        "    parser.add_argument(\n",
        "        \"--text_encoder_type\",\n",
        "        default=\"roberta-base\",\n",
        "        choices=(\"roberta-base\", \"distilroberta-base\", \"roberta-large\"),\n",
        "    )\n",
        "\n",
        "    # Backbone\n",
        "    parser.add_argument(\n",
        "        \"--backbone\",\n",
        "        default=\"resnet101\",\n",
        "        type=str,\n",
        "        help=\"Name of the convolutional backbone to use such as resnet50 resnet101 timm_tf_efficientnet_b3_ns\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--dilation\",\n",
        "        action=\"store_true\",\n",
        "        help=\"If true, we replace stride with dilation in the last convolutional block (DC5)\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--position_embedding\",\n",
        "        default=\"sine\",\n",
        "        type=str,\n",
        "        choices=(\"sine\", \"learned\"),\n",
        "        help=\"Type of positional embedding to use on top of the image features\",\n",
        "    )\n",
        "\n",
        "    # Transformer\n",
        "    parser.add_argument(\n",
        "        \"--enc_layers\",\n",
        "        default=6,\n",
        "        type=int,\n",
        "        help=\"Number of encoding layers in the transformer\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--dec_layers\",\n",
        "        default=6,\n",
        "        type=int,\n",
        "        help=\"Number of decoding layers in the transformer\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--dim_feedforward\",\n",
        "        default=2048,\n",
        "        type=int,\n",
        "        help=\"Intermediate size of the feedforward layers in the transformer blocks\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--hidden_dim\",\n",
        "        default=256,\n",
        "        type=int,\n",
        "        help=\"Size of the embeddings (dimension of the transformer)\",\n",
        "    )\n",
        "    parser.add_argument(\"--dropout\", default=0.1, type=float, help=\"Dropout applied in the transformer\")\n",
        "    parser.add_argument(\n",
        "        \"--nheads\",\n",
        "        default=8,\n",
        "        type=int,\n",
        "        help=\"Number of attention heads inside the transformer's attentions\",\n",
        "    )\n",
        "    parser.add_argument(\"--num_queries\", default=100, type=int, help=\"Number of query slots\")\n",
        "    parser.add_argument(\"--pre_norm\", action=\"store_true\")\n",
        "    parser.add_argument(\n",
        "        \"--no_pass_pos_and_query\",\n",
        "        dest=\"pass_pos_and_query\",\n",
        "        action=\"store_false\",\n",
        "        help=\"Disables passing the positional encodings to each attention layers\",\n",
        "    )\n",
        "\n",
        "    # Segmentation\n",
        "    parser.add_argument(\n",
        "        \"--mask_model\",\n",
        "        default=\"none\",\n",
        "        type=str,\n",
        "        choices=(\"none\", \"smallconv\", \"v2\"),\n",
        "        help=\"Segmentation head to be used (if None, segmentation will not be trained)\",\n",
        "    )\n",
        "    parser.add_argument(\"--remove_difficult\", action=\"store_true\")\n",
        "    parser.add_argument(\"--masks\", action=\"store_true\")\n",
        "\n",
        "    # Loss\n",
        "    parser.add_argument(\n",
        "        \"--no_aux_loss\",\n",
        "        dest=\"aux_loss\",\n",
        "        action=\"store_false\",\n",
        "        help=\"Disables auxiliary decoding losses (loss at each layer)\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--set_loss\",\n",
        "        default=\"hungarian\",\n",
        "        type=str,\n",
        "        choices=(\"sequential\", \"hungarian\", \"lexicographical\"),\n",
        "        help=\"Type of matching to perform in the loss\",\n",
        "    )\n",
        "\n",
        "    parser.add_argument(\"--contrastive_loss\", action=\"store_true\", help=\"Whether to add contrastive loss\")\n",
        "    parser.add_argument(\n",
        "        \"--no_contrastive_align_loss\",\n",
        "        dest=\"contrastive_align_loss\",\n",
        "        action=\"store_false\",\n",
        "        help=\"Whether to add contrastive alignment loss\",\n",
        "    )\n",
        "\n",
        "    parser.add_argument(\n",
        "        \"--contrastive_loss_hdim\",\n",
        "        type=int,\n",
        "        default=64,\n",
        "        help=\"Projection head output size before computing normalized temperature-scaled cross entropy loss\",\n",
        "    )\n",
        "\n",
        "    parser.add_argument(\n",
        "        \"--temperature_NCE\", type=float, default=0.07, help=\"Temperature in the  temperature-scaled cross entropy loss\"\n",
        "    )\n",
        "\n",
        "    # * Matcher\n",
        "    parser.add_argument(\n",
        "        \"--set_cost_class\",\n",
        "        default=1,\n",
        "        type=float,\n",
        "        help=\"Class coefficient in the matching cost\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--set_cost_bbox\",\n",
        "        default=5,\n",
        "        type=float,\n",
        "        help=\"L1 box coefficient in the matching cost\",\n",
        "    )\n",
        "    parser.add_argument(\n",
        "        \"--set_cost_giou\",\n",
        "        default=2,\n",
        "        type=float,\n",
        "        help=\"giou box coefficient in the matching cost\",\n",
        "    )\n",
        "    # Loss coefficients\n",
        "    parser.add_argument(\"--ce_loss_coef\", default=1, type=float)\n",
        "    parser.add_argument(\"--mask_loss_coef\", default=1, type=float)\n",
        "    parser.add_argument(\"--dice_loss_coef\", default=1, type=float)\n",
        "    parser.add_argument(\"--bbox_loss_coef\", default=5, type=float)\n",
        "    parser.add_argument(\"--giou_loss_coef\", default=2, type=float)\n",
        "    parser.add_argument(\"--qa_loss_coef\", default=1, type=float)\n",
        "    parser.add_argument(\n",
        "        \"--eos_coef\",\n",
        "        default=0.1,\n",
        "        type=float,\n",
        "        help=\"Relative classification weight of the no-object class\",\n",
        "    )\n",
        "    parser.add_argument(\"--contrastive_loss_coef\", default=0.1, type=float)\n",
        "    parser.add_argument(\"--contrastive_align_loss_coef\", default=1, type=float)\n",
        "\n",
        "    # Run specific\n",
        "\n",
        "    parser.add_argument(\"--test\", action=\"store_true\", help=\"Whether to run evaluation on val or test set\")\n",
        "    parser.add_argument(\"--test_type\", type=str, default=\"test\", choices=(\"testA\", \"testB\", \"test\"))\n",
        "    parser.add_argument(\"--output-dir\", default=\"\", help=\"path where to save, empty for no saving\")\n",
        "    parser.add_argument(\"--device\", default=\"cuda\", help=\"device to use for training / testing\")\n",
        "    parser.add_argument(\"--seed\", default=42, type=int)\n",
        "    parser.add_argument(\"--resume\", default=\"\", help=\"resume from checkpoint\")\n",
        "    parser.add_argument(\"--load\", default=\"\", help=\"resume from checkpoint\")\n",
        "    parser.add_argument(\"--start-epoch\", default=0, type=int, metavar=\"N\", help=\"start epoch\")\n",
        "    parser.add_argument(\"--eval\", action=\"store_true\", help=\"Only run evaluation\")\n",
        "    parser.add_argument(\"--num_workers\", default=5, type=int)\n",
        "\n",
        "    # Distributed training parameters\n",
        "    parser.add_argument(\"--world-size\", default=1, type=int, help=\"number of distributed processes\")\n",
        "    parser.add_argument(\"--dist-url\", default=\"env://\", help=\"url used to set up distributed training\")\n",
        "    return parser\n",
        "\n",
        "parser = argparse.ArgumentParser(\"DETR training and evaluation script\", parents=[get_args_parser()])\n",
        "args = parser.parse_args(['--batch_size', '1', '--dataset_config', 'configs/clevr.json', '--num_queries' ,'25',\n",
        "                          '--text_encoder_type' ,'distilroberta-base', '--backbone' ,'resnet18',\n",
        "                          '--resume' ,'https://zenodo.org/record/4721981/files/clevr_checkpoint.pth',\n",
        "                          '--eval'])\n",
        "d = vars(args)\n",
        "with open(args.dataset_config, \"r\") as f:\n",
        "    cfg = json.load(f)\n",
        "    d.update(cfg)\n",
        "print(args)    \n",
        "model, criterion, contrastive_criterion, qa_criterion, weight_dict = build_model(args)\n",
        "checkpoint = torch.hub.load_state_dict_from_url(args.resume, map_location=\"cpu\", check_hash=True)\n",
        "model.load_state_dict(checkpoint[\"model\"])\n",
        "model.cuda()\n",
        "model.eval()\n",
        "#model = torch.load('clevr_model.pt')\n",
        "#model.eval()\n"
      ],
      "metadata": {
        "id": "EUH34x1Do1S6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "750324b8ca7f4b4bbfc87935d293a571",
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            "f9abe09879cd464e8e962769f0a6c090",
            "c1a5d9b87a88413aa605013ca027c5c6",
            "7cea730bb0b44cb0ad50ea6e3d57da68",
            "6e5caa93afdd48ad9be4c5843bce41c8",
            "eb88071f14eb4d088839b9105867fabc",
            "365ae12a23654a8db694142a4985a8d5",
            "b6acdcc610694a908aae5501b1fe69c0",
            "9a8ea06ae3a943a59ed9f59b9a044592",
            "58cd588c514744e89406c4b1a25c58aa",
            "283f5369d8904fd9b40798a3fbe7fc7e",
            "6014a5a9b00840329298d9fc294f2eb1",
            "cdf0fb001b504044a912cdeb09cdf7eb",
            "6c7a7df5403541f2a628edbded19e087",
            "319e3a14dc57491488a03ec69490283a",
            "fd2db1433c6a4c50bb43500de6c69d96",
            "165c5a14b7a84d65a6501a6a09599bdf",
            "ac8f8e813e4448228a991d5bc166b58c",
            "f6bc1e7ff0ed414e96d25c4f24819a4d",
            "e53bbd131ac748fea19e401504db37ba",
            "84589f4c2fc647e4b365c20fc514f610",
            "7b602d5daca4440ba2a3970c0e48689c",
            "5bf89d7907364923b936a753a62a24ea",
            "31bffaff09a64fd1be051c8791cfd693",
            "e6c9736c06784e36aad72150a2cc8869",
            "ef43b2a0e8cf4dc28651ea4b4098926d",
            "f5e37b94c58c443ca756e9f20a95f0b7",
            "05f83780367f4b00ae9e882b6066594f",
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            "4c2b2511c17b41399a3f7f3132bed1ff",
            "e9a283ea8cd34dbe95acb2a6d94a55ca",
            "cec9eb0d61c44c2483816183183544e1",
            "ec7b38bf0e134f54a25c34f477c18ce3",
            "b8f59cfb2f0044a2996a407036ec4787",
            "ed0d9578cddd42009503c419258f95f5",
            "ff6394f2c79e4bc391c6b121baa4eeca",
            "bca28d9c1dfa41fa82fa676765d37265",
            "8665cb15d55343be8aa92552a0ce1bc8",
            "827b20841bc643c29a44ec857fac1869",
            "8666ed5865e041cca038e1e39b50f1b6"
          ]
        },
        "outputId": "bd4bc785-0b13-4471-c086-a8b6a6bdde9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(aux_loss=True, backbone='resnet18', batch_size=1, bbox_loss_coef=5, ce_loss_coef=1, clevr_ann_path='datasets/raw/clevr_annotations/full', clevr_img_path='datasets/raw/CLEVR_v1.0/images', clevr_variant='normal', clip_max_norm=0.1, coco_path='', combine_datasets=['clevr'], combine_datasets_val=['clevr'], contrastive_align_loss=True, contrastive_align_loss_coef=1, contrastive_loss=False, contrastive_loss_coef=0.1, contrastive_loss_hdim=64, dataset_config='configs/clevr.json', dec_layers=6, device='cuda', dice_loss_coef=1, dilation=False, dim_feedforward=2048, dist_url='env://', do_qa=1, dropout=0.1, ema=False, ema_decay=0.9998, enc_layers=6, eos_coef=0.1, epoch_chunks=-1, epochs=40, eval=True, eval_skip=1, fraction_warmup_steps=0.01, freeze_text_encoder=False, frozen_weights=None, giou_loss_coef=2, hidden_dim=256, load='', lr=0.0001, lr_backbone=1e-05, lr_drop=35, mask_loss_coef=1, mask_model='none', masks=False, modulated_lvis_ann_path='', nheads=8, no_detection=False, num_queries=25, num_workers=5, optimizer='adam', output_dir='', pass_pos_and_query=True, phrasecut_ann_path='', phrasecut_orig_ann_path='', position_embedding='sine', pre_norm=False, predict_final=False, qa_loss_coef=1, remove_difficult=False, resume='https://zenodo.org/record/4721981/files/clevr_checkpoint.pth', run_name='', schedule='linear_with_warmup', seed=42, set_cost_bbox=5, set_cost_class=1, set_cost_giou=2, set_loss='hungarian', split_qa_heads=1, start_epoch=0, temperature_NCE=0.07, test=False, test_type='test', text_encoder_lr=5e-05, text_encoder_type='distilroberta-base', vg_ann_path='', vg_img_path='', weight_decay=0.0001, world_size=1)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n"
          ]
        },
        {
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          "metadata": {}
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        {
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          "text": [
            "Downloading: \"https://zenodo.org/record/4721981/files/clevr_checkpoint.pth\" to /root/.cache/torch/hub/checkpoints/clevr_checkpoint.pth\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MDETR(\n",
              "  (transformer): Transformer(\n",
              "    (encoder): TransformerEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (4): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (5): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (decoder): TransformerDecoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerDecoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (cross_attn_image): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout3): Dropout(p=0.1, inplace=False)\n",
              "          (dropout4): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerDecoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (cross_attn_image): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout3): Dropout(p=0.1, inplace=False)\n",
              "          (dropout4): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerDecoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (cross_attn_image): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout3): Dropout(p=0.1, inplace=False)\n",
              "          (dropout4): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerDecoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (cross_attn_image): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout3): Dropout(p=0.1, inplace=False)\n",
              "          (dropout4): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (4): TransformerDecoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (cross_attn_image): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout3): Dropout(p=0.1, inplace=False)\n",
              "          (dropout4): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (5): TransformerDecoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (cross_attn_image): MultiheadAttention(\n",
              "            (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
              "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout3): Dropout(p=0.1, inplace=False)\n",
              "          (dropout4): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (text_encoder): RobertaModel(\n",
              "      (embeddings): RobertaEmbeddings(\n",
              "        (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
              "        (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "        (token_type_embeddings): Embedding(1, 768)\n",
              "        (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (encoder): RobertaEncoder(\n",
              "        (layer): ModuleList(\n",
              "          (0): RobertaLayer(\n",
              "            (attention): RobertaAttention(\n",
              "              (self): RobertaSelfAttention(\n",
              "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (output): RobertaSelfOutput(\n",
              "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (intermediate): RobertaIntermediate(\n",
              "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            )\n",
              "            (output): RobertaOutput(\n",
              "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (1): RobertaLayer(\n",
              "            (attention): RobertaAttention(\n",
              "              (self): RobertaSelfAttention(\n",
              "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (output): RobertaSelfOutput(\n",
              "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (intermediate): RobertaIntermediate(\n",
              "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            )\n",
              "            (output): RobertaOutput(\n",
              "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (2): RobertaLayer(\n",
              "            (attention): RobertaAttention(\n",
              "              (self): RobertaSelfAttention(\n",
              "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (output): RobertaSelfOutput(\n",
              "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (intermediate): RobertaIntermediate(\n",
              "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            )\n",
              "            (output): RobertaOutput(\n",
              "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (3): RobertaLayer(\n",
              "            (attention): RobertaAttention(\n",
              "              (self): RobertaSelfAttention(\n",
              "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (output): RobertaSelfOutput(\n",
              "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (intermediate): RobertaIntermediate(\n",
              "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            )\n",
              "            (output): RobertaOutput(\n",
              "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (4): RobertaLayer(\n",
              "            (attention): RobertaAttention(\n",
              "              (self): RobertaSelfAttention(\n",
              "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (output): RobertaSelfOutput(\n",
              "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (intermediate): RobertaIntermediate(\n",
              "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            )\n",
              "            (output): RobertaOutput(\n",
              "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (5): RobertaLayer(\n",
              "            (attention): RobertaAttention(\n",
              "              (self): RobertaSelfAttention(\n",
              "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (output): RobertaSelfOutput(\n",
              "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (intermediate): RobertaIntermediate(\n",
              "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            )\n",
              "            (output): RobertaOutput(\n",
              "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (pooler): RobertaPooler(\n",
              "        (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "        (activation): Tanh()\n",
              "      )\n",
              "    )\n",
              "    (resizer): FeatureResizer(\n",
              "      (fc): Linear(in_features=768, out_features=256, bias=True)\n",
              "      (layer_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "  )\n",
              "  (class_embed): Linear(in_features=256, out_features=256, bias=True)\n",
              "  (bbox_embed): MLP(\n",
              "    (layers): ModuleList(\n",
              "      (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "      (1): Linear(in_features=256, out_features=256, bias=True)\n",
              "      (2): Linear(in_features=256, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              "  (query_embed): Embedding(25, 256)\n",
              "  (qa_embed): Embedding(4, 256)\n",
              "  (input_proj): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "  (backbone): Joiner(\n",
              "    (0): Backbone(\n",
              "      (body): IntermediateLayerGetter(\n",
              "        (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "        (bn1): FrozenBatchNorm2d()\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "        (layer1): Sequential(\n",
              "          (0): BasicBlock(\n",
              "            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "          )\n",
              "          (1): BasicBlock(\n",
              "            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "        (layer2): Sequential(\n",
              "          (0): BasicBlock(\n",
              "            (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): FrozenBatchNorm2d()\n",
              "            )\n",
              "          )\n",
              "          (1): BasicBlock(\n",
              "            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "        (layer3): Sequential(\n",
              "          (0): BasicBlock(\n",
              "            (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): FrozenBatchNorm2d()\n",
              "            )\n",
              "          )\n",
              "          (1): BasicBlock(\n",
              "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "        (layer4): Sequential(\n",
              "          (0): BasicBlock(\n",
              "            (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): FrozenBatchNorm2d()\n",
              "            )\n",
              "          )\n",
              "          (1): BasicBlock(\n",
              "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn1): FrozenBatchNorm2d()\n",
              "            (relu): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (1): PositionEmbeddingSine()\n",
              "  )\n",
              "  (contrastive_align_projection_image): Linear(in_features=256, out_features=64, bias=True)\n",
              "  (contrastive_align_projection_text): Linear(in_features=256, out_features=64, bias=True)\n",
              "  (answer_type_head): Linear(in_features=256, out_features=3, bias=True)\n",
              "  (answer_binary_head): Linear(in_features=256, out_features=1, bias=True)\n",
              "  (answer_attr_head): Linear(in_features=256, out_features=15, bias=True)\n",
              "  (answer_reg_head): MLP(\n",
              "    (layers): ModuleList(\n",
              "      (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "      (1): Linear(in_features=256, out_features=256, bias=True)\n",
              "      (2): Linear(in_features=256, out_features=20, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Show example prediction"
      ],
      "metadata": {
        "id": "dwgRZTgGoiU1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ALL_ATTRIBUTES = [\n",
        "    \"small\",\n",
        "    \"large\",\n",
        "    \"gray\",\n",
        "    \"red\",\n",
        "    \"blue\",\n",
        "    \"green\",\n",
        "    \"brown\",\n",
        "    \"purple\",\n",
        "    \"cyan\",\n",
        "    \"yellow\",\n",
        "    \"cube\",\n",
        "    \"sphere\",\n",
        "    \"cylinder\",\n",
        "    \"rubber\",\n",
        "    \"metal\",\n",
        "]\n",
        "class_names = [\"no\", \"yes\", \"small\", \"large\", \"gray\", \"red\", \"blue\", \"green\", \"brown\", \"purple\", \"cyan\", \"yellow\", \"cube\", \"sphere\", \"cylinder\", \"rubber\",\n",
        "    \"metal\", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
      ],
      "metadata": {
        "id": "MGXbxkxzmX82"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    sample = dataset[5]\n",
        "    samples = torch.unsqueeze(sample[0], 0).cuda()\n",
        "    captions = [sample[1]['caption']]\n",
        "    memory_cache = model(samples, captions, encode_and_save=True)\n",
        "    outputs = model(samples, captions, encode_and_save=False, memory_cache=memory_cache)\n",
        "    print('true_answer_type:', sample[1]['answer_type'])\n",
        "    print('true_answer_bin:', sample[1]['answer_binary'])\n",
        "    print('true_answer_attr:', sample[1]['answer_attr'])\n",
        "    print('true_answer_reg:', sample[1]['answer_reg'])\n",
        "    print('---------------')\n",
        "    print('pred_answer_type: ', outputs[\"pred_answer_type\"].argmax(-1))\n",
        "    print('pred_answer_bin: ', int(outputs[\"pred_answer_binary\"].sigmoid() > 0.5))\n",
        "    print('pred_answer_attr: ', outputs[\"pred_answer_attr\"].argmax(-1))\n",
        "    print('pred_answer_reg: ', outputs[\"pred_answer_reg\"].argmax(-1))\n"
      ],
      "metadata": {
        "id": "SHKc68tYFjTS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98694b41-a982-4a14-ab9d-53a530d869d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "true_answer_type: tensor(2)\n",
            "true_answer_bin: tensor(0.)\n",
            "true_answer_attr: tensor(-100)\n",
            "true_answer_reg: tensor(2)\n",
            "---------------\n",
            "pred_answer_type:  tensor([2], device='cuda:0')\n",
            "pred_answer_bin:  1\n",
            "pred_answer_attr:  tensor([1], device='cuda:0')\n",
            "pred_answer_reg:  tensor([2], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def pred_answer(outputs):\n",
        "    idx = 0\n",
        "    ans_type = int(outputs[\"pred_answer_type\"].argmax(-1))\n",
        "    if ans_type == 0:\n",
        "        idx = int(outputs[\"pred_answer_binary\"].sigmoid() > 0.5)\n",
        "    elif ans_type == 1:\n",
        "        idx = int(outputs[\"pred_answer_attr\"].argmax(-1)) + 2\n",
        "    else:\n",
        "        idx = int(outputs[\"pred_answer_reg\"].argmax(-1)) + 17\n",
        "    return class_names[idx] \n",
        "        \n"
      ],
      "metadata": {
        "id": "_b6EEMIMmIuI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_answer(outputs)"
      ],
      "metadata": {
        "id": "0WiGuYsDm5s6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fbdbc42-06c7-4253-871f-5a08ddbf282f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###LIME"
      ],
      "metadata": {
        "id": "r_uuLcwRyupV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir datasets/lime_results"
      ],
      "metadata": {
        "id": "8hueiV9TNCkH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile limee.py\n",
        "\"\"\"Implementation of a wrapper around LIME.\n",
        "\n",
        "Handles implementation of the LIME (Local Interpretable Model-Agnostic Explanations)\n",
        "wrapper for the analysis of how important various parts of input are. Current implementation\n",
        "supports unimodal explanations with specified type for one sample, keeping other \n",
        "inputs constant.\n",
        "\"\"\"\n",
        "\n",
        "from typing import Callable, Dict\n",
        "\n",
        "import numpy as np\n",
        "from lime.lime_image import LimeImageExplainer\n",
        "from lime.lime_text import LimeTextExplainer\n",
        "\n",
        "\n",
        "class Lime:\n",
        "    \"\"\"Implementation of wrapper around LIME (Local Interpretable Model-Agnostic Explanations).\"\"\"\n",
        "\n",
        "    @classmethod\n",
        "    def explain_image_instance(\n",
        "        cls,\n",
        "        predictor_fn: Callable,\n",
        "        input_arr: np.ndarray,\n",
        "        init_params: Dict = {},\n",
        "        explanation_params: Dict = {},\n",
        "    ):\n",
        "        \"\"\"Explain image instance.\n",
        "\n",
        "        Args:\n",
        "            predictor_fn: Predictor function which takes in a batch of inputs (not single input).\n",
        "            input_arr: Input image as an array without a batch dimension.\n",
        "            init_params: Initialization parameters for LIME Explainer.\n",
        "            explanation_params: Parameters for LIME Explainer.\n",
        "\n",
        "        Returns:\n",
        "            lime.lime_image.ImageExplanation:\n",
        "                Explanation of the image with given arguments\n",
        "        \"\"\"\n",
        "\n",
        "        # Create LIME explainer\n",
        "        lime_explainer = LimeImageExplainer(**init_params)\n",
        "\n",
        "        # Run LIME explainer\n",
        "        exp = lime_explainer.explain_instance(\n",
        "            input_arr, predictor_fn, **explanation_params\n",
        "        )\n",
        "\n",
        "        return exp\n",
        "\n",
        "    @classmethod\n",
        "    def explain_text_instance(\n",
        "        cls,\n",
        "        predictor_fn: Callable,\n",
        "        text: str,\n",
        "        init_params: Dict = {},\n",
        "        explanation_params: Dict = {},\n",
        "    ):\n",
        "        \"\"\"Explain text instance.\n",
        "\n",
        "        Args:\n",
        "            predictor_fn: Predictor function which takes in a batch of inputs (not single input).\n",
        "            text: Input text as a string.\n",
        "            init_params: Initialization parameters for LIME Explainer.\n",
        "            explanation_params: Parameters for LIME Explainer.\n",
        "\n",
        "        Returns:\n",
        "            lime.lime_text.TextExplanation:\n",
        "                Explanation of the text with given arguments\n",
        "        \"\"\"\n",
        "\n",
        "        # Create LIME explainer\n",
        "        lime_explainer = LimeTextExplainer(**init_params)\n",
        "\n",
        "        # Run LIME explainer\n",
        "        exp = lime_explainer.explain_instance(text, predictor_fn, **explanation_params)\n",
        "\n",
        "        return exp\n"
      ],
      "metadata": {
        "id": "Q89cyT9uzpEX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "045a115f-17ec-496b-9342-b0d966b43b30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing limee.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from limee import Lime"
      ],
      "metadata": {
        "id": "6qH_G38KzqaP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"datasets/raw/CLEVR_v1.0/questions/CLEVR_val_questions.json\") as f:\n",
        "    q = json.load(f)"
      ],
      "metadata": {
        "id": "FdqIaUuMOQiH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "b8bb4267-350b-45cd-c437-858b4f8b2938"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-00053d9b65cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"datasets/raw/CLEVR_v1.0/questions/CLEVR_val_questions.json\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\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      2\u001b[0m     \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'datasets/raw/CLEVR_v1.0/questions/CLEVR_val_questions.json'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"datasets/raw/clevr_annotations/full/val.json\") as f:\n",
        "    a = json.load(f)"
      ],
      "metadata": {
        "id": "SeFSQAubYYok"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import PIL\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "RGqWRxkU-oY5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idx = 12000\n",
        "example = dataset[idx]\n",
        "correctansweridx = 0\n",
        "os.mkdir(f'datasets/lime_results/question_{idx:06d}')"
      ],
      "metadata": {
        "id": "B1KqYmSBp7zy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "info = example[1]\n",
        "question = info['caption']\n",
        "#image_id = int(info['image_id'])\n",
        "image_id = q[\"questions\"][idx]['image_index']\n",
        "answer_type = info['answer_type']\n",
        "answer_bin = info['answer_binary']\n",
        "answer_attr = info['answer_attr']\n",
        "answer_reg = info['answer_reg']\n",
        "#print(answer_type)\n",
        "#print(answer_bin)\n",
        "#print(answer_attr)\n",
        "#print(answer_reg)\n",
        "#print(image_id)\n",
        "if answer_type == 0:\n",
        "    ans = int(answer_bin)\n",
        "    if ans:\n",
        "        correctansweridx = 1\n",
        "    else:\n",
        "        correctansweridx = 0      \n",
        "elif answer_type == 1:\n",
        "    correctansweridx = int(answer_attr + 2)\n",
        "elif answer_type == 2:\n",
        "    correctansweridx = int(answer_reg + 17)\n",
        "\n",
        "samples = example[0].unsqueeze(0).cuda()\n",
        "captions = [question]\n",
        "memory_cache = model(samples, captions, encode_and_save=True)\n",
        "outputs = model(samples, captions, encode_and_save=False, memory_cache=memory_cache)\n",
        "print(question)\n",
        "print('answer: ', class_names[correctansweridx])\n",
        "print('pred_answer: ', pred_answer(outputs))    \n",
        "\n",
        "question_info = {'question': question, 'answer': class_names[correctansweridx], 'pred_answer': pred_answer(outputs)}\n",
        "with open(f'datasets/lime_results/question_{idx:06d}/question_info.json', \"w\") as outfile:\n",
        "    json.dump(question_info, outfile)"
      ],
      "metadata": {
        "id": "VNqFRqPH0Zs9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#image = plt.imread(f'datasets/raw/CLEVR_v1.0/images/val/CLEVR_val_{image_id:06d}.png')\n",
        "#plt.imshow(image)\n",
        "image = np.asarray(PIL.Image.open(f'datasets/raw/CLEVR_v1.0/images/val/CLEVR_val_{image_id:06d}.png'))\n",
        "#image = cv2.resize(image, (224, 224))\n",
        "image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)\n",
        "plt.imshow(image)\n",
        "Image.fromarray(image).save(f'datasets/lime_results/question_{idx:06d}/orig.png')\n"
      ],
      "metadata": {
        "id": "_cVjdGAXGHMN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir datasets/tmp"
      ],
      "metadata": {
        "id": "dJdT6V7lX2Ap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####running lime on image modality"
      ],
      "metadata": {
        "id": "o-MQ7qeM3b2p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from skimage.segmentation import mark_boundaries"
      ],
      "metadata": {
        "id": "hFvUz1--3ncm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets.transforms as T\n",
        "import copy\n",
        "import torch.nn.functional as F\n",
        "\n",
        "def make_clevr_transforms(cautious=False):\n",
        "\n",
        "    normalize = T.Compose([T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
        "\n",
        "    scales = [256, 288, 320, 352, 384]\n",
        "\n",
        "    return T.Compose(\n",
        "        [\n",
        "            # T.RandomResize([480], max_size=1333),\n",
        "            normalize,\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    raise ValueError(f\"unknown {image_set}\")\n",
        "\n",
        "def get_normed(img, target):\n",
        "    tmp = copy.deepcopy(target)\n",
        "    img, _ = make_clevr_transforms()(img, tmp)\n",
        "    return img\n",
        "    "
      ],
      "metadata": {
        "id": "X1xiYFub3bXQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 10\n",
        "num_samples = 2000\n",
        "\n",
        "def image_predictor_fn(images):\n",
        "    image_paths = []\n",
        "    for i in range(len(images)):\n",
        "        plt.imsave(f\"datasets/tmp/{i}.jpg\", images[i])\n",
        "        image_paths.append(f\"datasets/tmp/{i}.jpg\")\n",
        "\n",
        "    samples = []\n",
        "    for i in range(len(images)):\n",
        "        image = Image.open(f\"datasets/tmp/{i}.jpg\").convert('RGB')\n",
        "        normed_image = get_normed(image, info)\n",
        "        samples.append(normed_image)\n",
        "    samples = torch.stack(samples).cuda()  \n",
        "    captions = [question for i in range(batch_size)]  \n",
        "    with torch.no_grad():\n",
        "      memory_cache = model(samples, captions, encode_and_save=True)\n",
        "      outputs = model(samples, captions, encode_and_save=False, memory_cache=memory_cache)\n",
        "      pred_answer_binary_comp = torch.log(torch.sigmoid(1- outputs['pred_answer_binary']) / torch.sigmoid(outputs['pred_answer_binary']))\n",
        "      probas = torch.cat((outputs['pred_answer_binary'].unsqueeze(0).T, pred_answer_binary_comp.unsqueeze(0).T, outputs['pred_answer_attr'], outputs['pred_answer_reg']), 1)\n",
        "      return probas.cpu().detach().numpy()\n",
        "      #answer_type = (outputs['pred_answer_type'].argmax(-1))[0]\n",
        "\n",
        "        \n",
        "    "
      ],
      "metadata": {
        "id": "yjMciQpd3idx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "init_params = {}\n",
        "explanation_params = {\n",
        "    \"labels\": (correctansweridx,),\n",
        "    \"hide_color\": 0,\n",
        "    \"num_samples\": num_samples,\n",
        "    \"batch_size\": batch_size\n",
        "}\n",
        "image_exp = Lime.explain_image_instance(image_predictor_fn, np.array(image), init_params, explanation_params)"
      ],
      "metadata": {
        "id": "toAa8m6W4plQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp, mask = image_exp.get_image_and_mask(image_exp.top_labels[0], positive_only=False, num_features=10, hide_rest=False)\n",
        "img_boundry2 = mark_boundaries(temp/255.0, mask)\n",
        "plt.imshow(img_boundry2)\n",
        "plt.imsave(f'datasets/lime_results/question_{idx:06d}/lime_image.png', img_boundry2)"
      ],
      "metadata": {
        "id": "ebDgw6QFg8fc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(question)\n",
        "print('answer: ', class_names[correctansweridx])\n",
        "print('pred_answer: ', pred_answer(outputs))"
      ],
      "metadata": {
        "id": "8Nv-KomAneK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####running lime on text modality"
      ],
      "metadata": {
        "id": "wWhhSc3qW4CR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_samples = 100\n",
        "\n",
        "def text_predictor_fn(texts):\n",
        "    normed_image = get_normed(image, info)\n",
        "    samples = torch.stack([normed_image for i in range(len(texts))]).cuda()\n",
        "    with torch.no_grad():\n",
        "      memory_cache = model(samples, texts, encode_and_save=True)\n",
        "      outputs = model(samples, texts, encode_and_save=False, memory_cache=memory_cache)\n",
        "      pred_answer_binary_comp = torch.log(torch.sigmoid(1- outputs['pred_answer_binary']) / torch.sigmoid(outputs['pred_answer_binary']))\n",
        "      probas = torch.cat((outputs['pred_answer_binary'].unsqueeze(0).T, pred_answer_binary_comp.unsqueeze(0).T, outputs['pred_answer_attr'], outputs['pred_answer_reg']), 1)\n",
        "      return probas.cpu().detach().numpy()\n",
        "      \n"
      ],
      "metadata": {
        "id": "DrroObgsUcXj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "init_params = {\n",
        "    \"class_names\": class_names\n",
        "}\n",
        "explanation_params = {\n",
        "    \"labels\": (correctansweridx,),\n",
        "    \"num_samples\": num_samples,\n",
        "}\n",
        "text_exp = Lime.explain_text_instance(text_predictor_fn, question, init_params, explanation_params)"
      ],
      "metadata": {
        "id": "Ftx71bbPvYcp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_exp.local_exp"
      ],
      "metadata": {
        "id": "Bsa_YM72va6i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_fig = text_exp.as_pyplot_figure(label=correctansweridx)"
      ],
      "metadata": {
        "id": "rSCBnM-1wm2p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_fig.savefig(f'datasets/lime_results/question_{idx:06d}/lime_text')"
      ],
      "metadata": {
        "id": "OQcBg_AOQZJH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(question)\n",
        "print('answer: ', class_names[correctansweridx])\n",
        "print('pred_answer: ', pred_answer(outputs))"
      ],
      "metadata": {
        "id": "iBDwKj5ixyeC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###DIME preparation"
      ],
      "metadata": {
        "id": "haVq7p8d0SqD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir datasets/dime"
      ],
      "metadata": {
        "id": "fXGOKo8q9C5D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir datasets/dime/tmp"
      ],
      "metadata": {
        "id": "ADuA9QE49IZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idxs = [i*1000 for i in range(100)]\n",
        "imgs = []\n",
        "questions = []\n",
        "\n",
        "for idx in idxs:\n",
        "    question_info = q[\"questions\"][idx]\n",
        "    image_id = question_info['image_index']\n",
        "    question = question_info['question']\n",
        "    question_id = question_info['question_index']\n",
        "    answer = question_info['answer']\n",
        "    program = question_info['program']\n",
        "    img = PIL.Image.open(f'datasets/raw/CLEVR_v1.0/images/val/CLEVR_val_{image_id:06d}.png').convert('RGB')\n",
        "    normed_image = get_normed(img, info)\n",
        "\n",
        "    imgs.append(normed_image)\n",
        "    questions.append(question)"
      ],
      "metadata": {
        "id": "HPe6DrZP0ZQS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "storage = torch.zeros(100,100,len(class_names)).cuda()\n",
        "\n",
        "with torch.no_grad():\n",
        "  for id1 in idxs:\n",
        "    for id2 in idxs:\n",
        "      print(\"At \"+str(id1)+\" \"+str(id2))\n",
        "      samples = imgs[id1//1000].unsqueeze(0).cuda()\n",
        "      captions = [questions[id2//1000]]\n",
        "      memory_cache = model(samples, captions, encode_and_save=True)\n",
        "      outputs = model(samples, captions, encode_and_save=False, memory_cache=memory_cache)\n",
        "      pred_answer_binary_comp = torch.log(torch.sigmoid(1- outputs['pred_answer_binary']) / torch.sigmoid(outputs['pred_answer_binary']))\n",
        "      probas = torch.cat((outputs['pred_answer_binary'].unsqueeze(0).T, pred_answer_binary_comp.unsqueeze(0).T, outputs['pred_answer_attr'], outputs['pred_answer_reg']), 1)\n",
        "\n",
        "      storage[id1//1000][id2//1000]=probas\n",
        "\n",
        "torch.save(storage, 'datasets/dime/storage.pt')\n",
        "#storage = torch.load('datasets/dime/storage.pt')"
      ],
      "metadata": {
        "id": "OIXlKwYl8FBo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Runing DIME"
      ],
      "metadata": {
        "id": "v2GTrWtJBYXs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####DIME on image"
      ],
      "metadata": {
        "id": "2ZmXtuzaIXZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def imgemape(inps):\n",
        "  image_paths = []\n",
        "  rets = []\n",
        "  for ii in range(len(inps)):\n",
        "    plt.imsave(f\"datasets/dime/tmp/{ii}.jpg\", inps[ii])\n",
        "    image_paths.append(f\"datasets/dime/tmp/{ii}.jpg\")\n",
        "    img = PIL.Image.open(image_paths[ii]).convert('RGB')\n",
        "    normed_image = get_normed(img, info)\n",
        "    samples = normed_image.unsqueeze(0).cuda()\n",
        "\n",
        "    with torch.no_grad():\n",
        "      origs=storage[:,:,correctansweridx]\n",
        "      newstorage = copy.deepcopy(origs)\n",
        "      for j in range(100):\n",
        "        memory_cache = model(samples, captions, encode_and_save=True)\n",
        "        outputs = model(samples, captions, encode_and_save=False, memory_cache=memory_cache)\n",
        "        pred_answer_binary_comp = torch.log(torch.sigmoid(1- outputs['pred_answer_binary']) / torch.sigmoid(outputs['pred_answer_binary']))\n",
        "        probas = torch.cat((outputs['pred_answer_binary'].unsqueeze(0).T, pred_answer_binary_comp.unsqueeze(0).T, outputs['pred_answer_attr'], outputs['pred_answer_reg']), 1)\n",
        "        newstorage[idx//1000][j]=probas[0][correctansweridx]\n",
        "      avg0=torch.mean(newstorage, dim=1)\n",
        "      avg1=torch.mean(newstorage, dim=0)\n",
        "      avg = torch.mean(avg0)\n",
        "      uniout = (avg0[idx//1000]+avg1[idx//1000]-avg).item()\n",
        "      multiout = (newstorage[idx//1000][idx//1000]-uniout).item()\n",
        "      \n",
        "      rets.append([uniout,multiout])\n",
        "  os.system(\"rm datasets/dime/tmp/*.jpg\")\n",
        "  return np.array(rets)\n",
        "\n"
      ],
      "metadata": {
        "id": "NKSUc8qg_XT1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "init_params = {}\n",
        "explanation_params = {\n",
        "    \"labels\": (0,1),\n",
        "    \"hide_color\": 0,\n",
        "    \"num_samples\": num_samples,\n",
        "    \"batch_size\": batch_size\n",
        "}\n",
        "image_exp = Lime.explain_image_instance(imgemape, np.array(image), init_params, explanation_params)"
      ],
      "metadata": {
        "id": "WQnPLeYLG4yT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unimodal output"
      ],
      "metadata": {
        "id": "5KEeloZTJdiG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp, mask = image_exp.get_image_and_mask(0, positive_only=False, num_features=10, hide_rest=False)\n",
        "img_boundry2 = mark_boundaries(temp/255.0, mask)\n",
        "plt.imshow(img_boundry2)\n",
        "plt.imsave(f'datasets/lime_results/question_{idx:06d}/uni_image.png', img_boundry2)"
      ],
      "metadata": {
        "id": "de1eAKXVHQGu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multimodal output"
      ],
      "metadata": {
        "id": "WfATjFknJgpL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp, mask = image_exp.get_image_and_mask(1, positive_only=False, num_features=10, hide_rest=False)\n",
        "img_boundry2 = mark_boundaries(temp/255.0, mask)\n",
        "plt.imshow(img_boundry2)\n",
        "plt.imsave(f'datasets/lime_results/question_{idx:06d}/multi_image.png', img_boundry2)"
      ],
      "metadata": {
        "id": "wocDYNt0HWD-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####DIME on text"
      ],
      "metadata": {
        "id": "85iuVKJ_IbIf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def txtemape(inps):\n",
        "  rets = []\n",
        "  for ii in range(len(inps)):\n",
        "    captions = inps[ii:ii+1]\n",
        "    with torch.no_grad():\n",
        "      origs=storage[:,:,correctansweridx]\n",
        "      newstorage = copy.deepcopy(origs)\n",
        "      for j in range(100):\n",
        "        memory_cache = model(samples, captions, encode_and_save=True)\n",
        "        outputs = model(samples, captions, encode_and_save=False, memory_cache=memory_cache)\n",
        "        pred_answer_binary_comp = torch.log(torch.sigmoid(1- outputs['pred_answer_binary']) / torch.sigmoid(outputs['pred_answer_binary']))\n",
        "        probas = torch.cat((outputs['pred_answer_binary'].unsqueeze(0).T, pred_answer_binary_comp.unsqueeze(0).T, outputs['pred_answer_attr'], outputs['pred_answer_reg']), 1)\n",
        "        newstorage[j][idx//1000]=probas[0][correctansweridx]\n",
        "      avg0=torch.mean(newstorage, dim=1)\n",
        "      avg1=torch.mean(newstorage, dim=0)\n",
        "      avg = torch.mean(avg0)\n",
        "      uniout = (avg0[idx//1000]+avg1[idx//1000]-avg).item()\n",
        "      multiout = (newstorage[idx//1000][idx//1000]-uniout).item()\n",
        "      \n",
        "      rets.append([uniout,multiout])\n",
        "      \n",
        "  return np.array(rets)"
      ],
      "metadata": {
        "id": "JaLTdOdWIag5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "init_params = {}\n",
        "explanation_params = {\n",
        "    \"labels\": (0,1),\n",
        "    \"num_samples\":100\n",
        "}\n",
        "text_exp = Lime.explain_text_instance(txtemape, question, init_params, explanation_params)"
      ],
      "metadata": {
        "id": "EVOJxC7ZKHSN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unimodal output"
      ],
      "metadata": {
        "id": "h3DZpmNsKiDJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_fig = text_exp.as_pyplot_figure(label=0)"
      ],
      "metadata": {
        "id": "DqJdG4xqKQsV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_fig.savefig(f'datasets/lime_results/question_{idx:06d}/uni_text.png')"
      ],
      "metadata": {
        "id": "s4Um1BamRKcN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multimodal output"
      ],
      "metadata": {
        "id": "dKX3wgYQKjoD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_fig = text_exp.as_pyplot_figure(label=1)"
      ],
      "metadata": {
        "id": "WXUx4zb1Kcne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_fig.savefig(f'datasets/lime_results/question_{idx:06d}/multi_text.png')"
      ],
      "metadata": {
        "id": "pw1uFpZHRTkD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "q65cMdz_Rhti"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/drive/MyDrive/mdetr"
      ],
      "metadata": {
        "id": "zd0ao-ydRwNw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -R datasets/lime_results /content/drive/MyDrive/mdetr"
      ],
      "metadata": {
        "id": "xNbeh_oFR3Yy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}