GestaltMML_Inference.ipynb

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      "metadata": {
        "id": "5iTmf7OifiY1"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import json\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sample Texts:\n",
        "\n",
        "'Sex male Age 8 years 0 months Ethnicity European obsolete Sparse  thin eyebrow Sparse eyelashes Cutaneous photosensitivity Poikiloderma Sparse scalp hair Lower-limb joint contracture Lower limb muscle weakness Sparse hair Palmoplantar erythema Abnormality on pulmonary function testing Sparse eyebrow'\n",
        "\n",
        "\n",
        "'Sex female Age 1 years 8 months Ethnicity European Visual impairment Delayed speech  language development Hypotonia Global developmental delay Delayed gross motor development Intellectual disability severe Abnormality of the digestive system'\n",
        "\n",
        "'Sex male Age 3 years 6 months Ethnicity European Long palpebral fissure Growth delay Decreased body weight Eversion of lateral third of lower eyelids Feeding difficulties'"
      ],
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        "id": "vnayQ1l8DmM_"
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          "data": {
            "text/plain": [
              "528"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "#528 total disease labels in GestaltMML v1.0.9. DO NOT MODIFY \"528\"\n",
        "num_list = [int(i) for i in range(528)]\n",
        "label2id = dict(zip(num_list,num_list))\n",
        "id2label = dict(zip(num_list,num_list))\n",
        "len(label2id)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sample input texts: \"Sex male Age 3 years 6 months Ethnicity European Long palpebral fissure Growth delay Decreased body weight Eversion of lateral third of lower eyelids Feeding difficulties\""
      ],
      "metadata": {
        "id": "RaOY-OsZ9upX"
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      "source": [
        "#If you don't care about the accuracy or do not know the true label, then \"label\" does not matter. You can use any number from 0 to 527\n",
        "columns = ['image_id', 'filename', 'texts', 'label']\n",
        "data = [(461, '461_aligned.jpg','Sex male Age 3 years 6 months Ethnicity European Long palpebral fissure Growth delay Decreased body weight Eversion of lateral third of lower eyelids Feeding difficulties',269),\n",
        "        (477, '477_aligned.jpg','Sex male Age 8 years 0 months Ethnicity European obsolete Sparse thin eyebrow Sparse eyelashes Cutaneous photosensitivity Poikiloderma Sparse scalp hair Lower-limb joint contracture Lower limb muscle weakness Sparse hair Palmoplantar erythema Abnormality on pulmonary function testing Sparse eyebrow',269),\n",
        "        (479, '479_aligned.jpg','Sex female Age 1 years 8 months Ethnicity European Visual impairment Delayed speech language development Hypotonia Global developmental delay Delayed gross motor development Intellectual disability severe Abnormality of the digestive system',269)]\n",
        "total_test = pd.DataFrame(data, columns=columns)\n",
        "total_test"
      ],
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            "text/plain": [
              "   image_id         filename  \\\n",
              "0       461  461_aligned.jpg   \n",
              "1       477  477_aligned.jpg   \n",
              "2       479  479_aligned.jpg   \n",
              "\n",
              "                                               texts  label  \n",
              "0  Sex male Age 3 years 6 months Ethnicity Europe...    269  \n",
              "1  Sex male Age 8 years 0 months Ethnicity Europe...    269  \n",
              "2  Sex female Age 1 years 8 months Ethnicity Euro...    269  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "total_test",
              "summary": "{\n  \"name\": \"total_test\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"image_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 461,\n        \"max\": 479,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          461,\n          477,\n          479\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filename\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"461_aligned.jpg\",\n          \"477_aligned.jpg\",\n          \"479_aligned.jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"texts\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Sex male Age 3 years 6 months Ethnicity European Long palpebral fissure Growth delay Decreased body weight Eversion of lateral third of lower eyelids Feeding difficulties\",\n          \"Sex male Age 8 years 0 months Ethnicity European obsolete Sparse thin eyebrow Sparse eyelashes Cutaneous photosensitivity Poikiloderma Sparse scalp hair Lower-limb joint contracture Lower limb muscle weakness Sparse hair Palmoplantar erythema Abnormality on pulmonary function testing Sparse eyebrow\",\n          \"Sex female Age 1 years 8 months Ethnicity European Visual impairment Delayed speech language development Hypotonia Global developmental delay Delayed gross motor development Intellectual disability severe Abnormality of the digestive system\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 269,\n        \"max\": 269,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          269\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
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    {
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      "execution_count": 8,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'image_id': 461,\n",
              " 'question': 'Sex male Age 3 years 6 months Ethnicity European Long palpebral fissure Growth delay Decreased body weight Eversion of lateral third of lower eyelids Feeding difficulties'}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "test_questions = []\n",
        "for i in range(len(total_test)):\n",
        "    temp_dic = {'image_id':total_test.loc[i,'image_id'],'question':total_test.loc[i,'texts']}\n",
        "    test_questions.append(temp_dic)\n",
        "\n",
        "test_questions[0]"
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      "metadata": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'labels': [269], 'scores': [1]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "test_annotations = []\n",
        "for i in range(len(total_test)):\n",
        "    temp_dic = {'labels':[label2id[total_test.loc[i,'label']]],'scores':[1]}\n",
        "    test_annotations.append(temp_dic)\n",
        "\n",
        "test_annotations[0]"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'/content/drive/MyDrive/GestaltMML/test_images/461_aligned.jpg': 461,\n",
              " '/content/drive/MyDrive/GestaltMML/test_images/477_aligned.jpg': 477,\n",
              " '/content/drive/MyDrive/GestaltMML/test_images/479_aligned.jpg': 479}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#replace root by your own file path\n",
        "root = '/content/drive/MyDrive/GestaltMML/test_images'\n",
        "filename_to_id = {root + \"/\" + total_test.loc[i,'filename']: total_test.loc[i,'image_id'] for i in range(len(total_test))}\n",
        "id_to_filename = {v:k for k,v in filename_to_id.items()}\n",
        "filename_to_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "01rcqZDBzskw",
        "outputId": "21475d24-1b08-46d9-cff2-fc1b7588da30"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=112x112>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "#Sample Image\n",
        "from PIL import Image\n",
        "path = id_to_filename[test_questions[0]['image_id']]\n",
        "image = Image.open(path)\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296,
          "referenced_widgets": [
            "166215b453c0447ea51069acd1926df4",
            "9c10b936df444840a88cc20f1ee195e6",
            "c74238ae6a5b4561bb9a15763aca3dd6",
            "f5ca346db9f443348369c302cfe749c5",
            "d47ed2f7924a420388af59704602f68d",
            "268455da53294d5b85195e1980c5a039",
            "1977ba691f14407eadf2c18101a4e924",
            "fd3872bdc96744efb458f12ff8c6b2ca",
            "985060c78d934d049040cb2b9ccb959b",
            "7adee9677e4946619f8d59cda639426b",
            "99a1be567967466fb3e09e0501edf28d",
            "2457eb6a290c425085bd33b9fc2c5db7",
            "b65bccfd2bbc44348c48c64e95d83165",
            "a346ba0271ff456d8b4945ce8aa654fe",
            "379166b5740d48399deb0a20ce365648",
            "22f515744b8d4b958b9d7226ada66f9b",
            "e026e8dbe6314f37b5f76d60bd271d2f",
            "d8f5a04c76304c658f7af81034e84cf2",
            "fb35002e43f64f51a068af66d57b8c9d",
            "58a81efac3df4d36ba03350d9b17cf03",
            "a5214120c0394ba584dac937500f790e",
            "dd6c7425120d4f269518ac72e15518a6",
            "2820dca83a88497d9e0ce9a5e56d3a64",
            "cae40932cce54d59b12702858dd54fca",
            "6673a22939ad48f7bea9055d8dd58ab5",
            "6a498bb0e5e544bbbec8e1f378be2926",
            "1f5629198eef4442a6478ee7a8873110",
            "9d99b7b759b44fea96b6a29207f2c826",
            "d768b95e10d44e90afb4a640f27a228f",
            "7e62fc0abf8c44a09a99fcbf9761d8e4",
            "9557c7532ba84fe88e6d56e9262c9b24",
            "b887722a400943369c3df521182ffb08",
            "5faf0b7163234ff9af0256f59ddbc5ec",
            "e2dd3416934c427892b2d2bed6c4ba5b",
            "14598a90b5384d1bbc5cb7c21497e9b5",
            "e978afa82db5486087d7e7fdcd7f08e4",
            "7f1b11066f894118b5be5f3c5be37314",
            "72c48e88efb740bfb67ccc5d90f0153c",
            "615e24d6933748d9bab654538d7b320f",
            "0bb1e4ffca7a4504a46658ae768145ad",
            "73717252a4784c29b795eb41d431007a",
            "0290764e495e46f693a293bb776dca7d",
            "06ce8170a6494961a60d55c30e389609",
            "26982ed974334d18bbac262064d4bdf2",
            "5fb4442baeb042df8bedc6f9e1734b9e",
            "c8f86aae87074a97a4e0b3a8a486f906",
            "75eb8b87648a4002bb10cc91fcd5f905",
            "967eb714507944b082bef0cb6e96fb96",
            "0f44ddb3549e4104a2cec1b0f299251a",
            "aa34039a9d1444509352656d225f1756",
            "92937107424b428fa8b1311c50490c5f",
            "b0da913f010d4567b3a1031eaac13fac",
            "619535022a3b4d87b1335c0c676497d2",
            "7229584db2c54693ba84b63b49da494b",
            "593f0a38f4e84f7c8aac2e7560aba2d6"
          ]
        },
        "id": "Cjz6_vES0Awy",
        "outputId": "dae36711-a1a3-44cb-ae99-cf5c76c51ed0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/251 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "166215b453c0447ea51069acd1926df4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/320 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2457eb6a290c425085bd33b9fc2c5db7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2820dca83a88497d9e0ce9a5e56d3a64"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2dd3416934c427892b2d2bed6c4ba5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5fb4442baeb042df8bedc6f9e1734b9e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import ViltProcessor\n",
        "processor = ViltProcessor.from_pretrained(\"dandelin/vilt-b32-mlm\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "6WpdeTnR0Yef"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from PIL import Image\n",
        "\n",
        "class VQADataset(torch.utils.data.Dataset):\n",
        "    \"\"\"VQA (v2) dataset.\"\"\"\n",
        "\n",
        "    def __init__(self, questions, annotations, processor):\n",
        "        self.questions = questions\n",
        "        self.annotations = annotations\n",
        "        self.processor = processor\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.annotations)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # get image + text - training pairs\n",
        "        annotation = self.annotations[idx]\n",
        "        questions = self.questions[idx]\n",
        "        image = Image.open(id_to_filename[questions['image_id']])\n",
        "        text = questions['question']\n",
        "        #send text and image through processor\n",
        "        encoding = self.processor(image, text, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
        "        # remove batch dimension\n",
        "        for k,v in encoding.items():\n",
        "            encoding[k] = v.squeeze()\n",
        "        # add labels: true disease & scores: confidence\n",
        "        labels = annotation['labels']\n",
        "        scores = annotation['scores']\n",
        "        # based on: https://github.com/dandelin/ViLT/blob/762fd3975c180db6fc88f577cf39549983fa373a/vilt/modules/objectives.py#L301\n",
        "        # https://github.com/jnhwkim/ban-vqa/blob/master/train.py#L19\n",
        "        # targets = [0] * number of disease labels\n",
        "        targets = torch.zeros(len(id2label))\n",
        "        for label, score in zip(labels, scores):\n",
        "              targets[label] = score\n",
        "        # one-hot encoding in our case\n",
        "        encoding[\"labels\"] = targets\n",
        "\n",
        "        return encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "f31e55d485e34a389c989943ac46d459",
            "658bd3e5986c42708252fac7d012715d",
            "93ac3e448c9f46aa9a867aac2dab59fb",
            "3694c0e65a524710825114fa5c34fcf4",
            "8edc86a7e15048f1af4784e5a912c1e0",
            "fda926fa64c843a5ae0b52976bd6f789",
            "d3d33b060d4948d088341923663b4872",
            "72061992d75f412e96f3f728017083b9",
            "47af74fae1444469a02a2ac18fc4eb56",
            "ca2f73e797674a2cbcdc97657928a620",
            "764fa50d774e456fa5a87afcbede01e7",
            "8b0245d54e03420cb0395189adf4fc06",
            "45a850bd4efd41f5b078d66ed7554267",
            "b98261ab2e994248998cfdcd9461d239",
            "2e95091e41524e4b98e515953bcefe92",
            "824ab34e5c6044419507995325c3a28a",
            "99a504ac597f4e1787f2878f83be912c",
            "f96bfe3d33b445dea4d3cfe5109b8bfb",
            "6124e4a29322418f8d79cbfb0fe7ec1b",
            "c50b8ff798da4dfd8d9eb8906098cb41",
            "b0920f7d095b4b11b4088b392a049db8",
            "bc21701403b34bf89e332fdd25f4f7c1"
          ]
        },
        "id": "T6T96GKq2LrV",
        "outputId": "f6454b13-1873-4504-dd6d-3493f32b3d53"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of ViltForQuestionAnswering were not initialized from the model checkpoint at dandelin/vilt-b32-mlm and are newly initialized: ['classifier.0.weight', 'classifier.1.weight', 'classifier.1.bias', 'classifier.0.bias', 'classifier.3.weight', 'classifier.3.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "DataParallel(\n",
              "  (module): ViltForQuestionAnswering(\n",
              "    (vilt): ViltModel(\n",
              "      (embeddings): ViltEmbeddings(\n",
              "        (text_embeddings): TextEmbeddings(\n",
              "          (word_embeddings): Embedding(30522, 768)\n",
              "          (position_embeddings): Embedding(40, 768)\n",
              "          (token_type_embeddings): Embedding(2, 768)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (patch_embeddings): ViltPatchEmbeddings(\n",
              "          (projection): Conv2d(3, 768, kernel_size=(32, 32), stride=(32, 32))\n",
              "        )\n",
              "        (token_type_embeddings): Embedding(2, 768)\n",
              "        (dropout): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (encoder): ViltEncoder(\n",
              "        (layer): ModuleList(\n",
              "          (0-11): 12 x ViltLayer(\n",
              "            (attention): ViltAttention(\n",
              "              (attention): ViltSelfAttention(\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=0.0, inplace=False)\n",
              "              )\n",
              "              (output): ViltSelfOutput(\n",
              "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (intermediate): ViltIntermediate(\n",
              "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (intermediate_act_fn): GELUActivation()\n",
              "            )\n",
              "            (output): ViltOutput(\n",
              "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (pooler): ViltPooler(\n",
              "        (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "        (activation): Tanh()\n",
              "      )\n",
              "    )\n",
              "    (classifier): Sequential(\n",
              "      (0): Linear(in_features=768, out_features=1536, bias=True)\n",
              "      (1): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "      (2): GELU(approximate='none')\n",
              "      (3): Linear(in_features=1536, out_features=528, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import ViltForQuestionAnswering\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = ViltForQuestionAnswering.from_pretrained(\"dandelin/vilt-b32-mlm\",\n",
        "                                                 num_labels=len(id2label),\n",
        "                                                 id2label=id2label,\n",
        "                                                 label2id=label2id)\n",
        "#model= nn.DataParallel(model)\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNPRswq_2c7p"
      },
      "source": [
        "## Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "n1VxE9T_9Zlz",
        "outputId": "1b1659ef-ddb7-4e47-ba14-2fba7cc87569",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ViltForQuestionAnswering were not initialized from the model checkpoint at dandelin/vilt-b32-mlm and are newly initialized: ['classifier.0.bias', 'classifier.0.weight', 'classifier.1.bias', 'classifier.1.weight', 'classifier.3.bias', 'classifier.3.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ViltForQuestionAnswering(\n",
              "  (vilt): ViltModel(\n",
              "    (embeddings): ViltEmbeddings(\n",
              "      (text_embeddings): TextEmbeddings(\n",
              "        (word_embeddings): Embedding(30522, 768)\n",
              "        (position_embeddings): Embedding(40, 768)\n",
              "        (token_type_embeddings): Embedding(2, 768)\n",
              "        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (patch_embeddings): ViltPatchEmbeddings(\n",
              "        (projection): Conv2d(3, 768, kernel_size=(32, 32), stride=(32, 32))\n",
              "      )\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): ViltEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x ViltLayer(\n",
              "          (attention): ViltAttention(\n",
              "            (attention): ViltSelfAttention(\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=0.0, inplace=False)\n",
              "            )\n",
              "            (output): ViltSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ViltIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ViltOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (pooler): ViltPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Linear(in_features=768, out_features=1536, bias=True)\n",
              "    (1): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "    (2): GELU(approximate='none')\n",
              "    (3): Linear(in_features=1536, out_features=528, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "from transformers import ViltForQuestionAnswering\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = ViltForQuestionAnswering.from_pretrained(\"dandelin/vilt-b32-mlm\",\n",
        "                                                 num_labels=len(id2label),\n",
        "                                                 id2label=id2label,\n",
        "                                                 label2id=label2id)\n",
        "#load the model weights GestaltMML_model.pt. Email dawu@sas.upenn.edu/jeremywu12345@gmail.com for model weights if you have access to GMDB.\n",
        "PATH = '/content/drive/MyDrive/GestaltMML/GestaltMML_model.pt'\n",
        "#If you have GPU device, remove \"map_location=torch.device('cpu')\" below. If you only use CPU, keep as below.\n",
        "model.load_state_dict(torch.load(PATH,map_location=torch.device('cpu')))\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_4Snnpz-2axI"
      },
      "outputs": [],
      "source": [
        "test_dataset = VQADataset(questions = test_questions,\n",
        "                     annotations = test_annotations,\n",
        "                     processor=processor)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "#Disease Dictionary: Label ID -> real diesease name\n",
        "with open('/content/drive/MyDrive/GestaltMML/disease_dict.json') as json_file:\n",
        "    disease_dict = json.load(json_file)\n",
        "disease_dict[str(1)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "id": "8Lv44p2o6RxK",
        "outputId": "1aecfb42-6b76-47ae-e6f8-b4c627ffc450"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'WILLIAMS-BEUREN SYNDROME; WBS'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "aFi8XCiB2nzW",
        "outputId": "6bc995f7-9d13-4873-b01f-4816a85a216d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{461: 'Hypoplastic left heart syndrome',\n",
              " 477: 'POIKILODERMA, HEREDITARY FIBROSING, WITH TENDON CONTRACTURES, MYOPATHY, AND PULMONARY FIBROSIS; POIKTMP',\n",
              " 479: 'MENTAL RETARDATION, AUTOSOMAL DOMINANT 54; MRD54'}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "#Top-1 most likely diseases predicted by GestaltMML\n",
        "res = {}\n",
        "for i in range(len(test_dataset)):\n",
        "    example = test_dataset[i]\n",
        "    example = {k: v.unsqueeze(0).to(device) for k,v in example.items()}\n",
        "    outputs = model(**example)\n",
        "    logits = outputs.logits\n",
        "    predicted_class = logits.argmax(-1).item()\n",
        "    res[total_test.loc[i,'image_id']] = disease_dict[str(id2label[predicted_class])]\n",
        "res"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Top-10 most likely diseases for each image_id\n",
        "res = {}\n",
        "for i in range(len(test_dataset)):\n",
        "  example = test_dataset[i]\n",
        "  example = {k: v.unsqueeze(0).to(device) for k,v in example.items()}\n",
        "  outputs = model(**example)\n",
        "  logits = outputs.logits\n",
        "  #Replace 10 by other number N>1 to test Top-N most likely diseases\n",
        "  predicted_class = torch.topk(logits,10).indices.flatten().tolist()\n",
        "  disease_list = [disease_dict[str(id2label[i])] for i in predicted_class]\n",
        "  res[total_test.loc[i,'image_id']] = disease_list\n",
        "res_df = pd.DataFrame(res)\n",
        "res_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "IvyB4DRbgsKm",
        "outputId": "48328214-f43e-4594-9d9a-fe7f9e32dfd7"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 461  \\\n",
              "0                    Hypoplastic left heart syndrome   \n",
              "1  DEVELOPMENTAL AND EPILEPTIC ENCEPHALOPATHY 83;...   \n",
              "2     MANDIBULOFACIAL DYSOSTOSIS WITH ALOPECIA; MFDA   \n",
              "3                                      LIG4 SYNDROME   \n",
              "4  SHORT STATURE, BRACHYDACTYLY, IMPAIRED INTELLE...   \n",
              "5                     NEUROFIBROMATOSIS, TYPE I; NF1   \n",
              "6        BOSMA ARHINIA MICROPHTHALMIA SYNDROME; BAMS   \n",
              "7  INTELLECTUAL DEVELOPMENTAL DISORDER, X-LINKED ...   \n",
              "8  MICROCEPHALY, GROWTH DEFICIENCY, SEIZURES, AND...   \n",
              "9                   RADIO-TARTAGLIA SYNDROME; RATARS   \n",
              "\n",
              "                                                 477  \\\n",
              "0  POIKILODERMA, HEREDITARY FIBROSING, WITH TENDO...   \n",
              "1                          Rothmund-Thomson syndrome   \n",
              "2                      Blepharocheilodontic syndrome   \n",
              "3  Microcephaly, growth restriction and increased...   \n",
              "4                                      LIG4 SYNDROME   \n",
              "5                      Focal facial dermal dysplasia   \n",
              "6                 BRANCHIOOCULOFACIAL SYNDROME; BOFS   \n",
              "7                        Hutchinson-Gilford progeria   \n",
              "8                                               CDH8   \n",
              "9                   FONTAINE PROGEROID SYNDROME; FPS   \n",
              "\n",
              "                                                 479  \n",
              "0   MENTAL RETARDATION, AUTOSOMAL DOMINANT 54; MRD54  \n",
              "1  INTELLECTUAL DEVELOPMENTAL DISORDER, AUTOSOMAL...  \n",
              "2                        SMITH-MAGENIS SYNDROME; SMS  \n",
              "3                    PHELAN-MCDERMID SYNDROME; PHMDS  \n",
              "4                        PITT-HOPKINS SYNDROME; PTHS  \n",
              "5                      HAO-FOUNTAIN SYNDROME; HAFOUS  \n",
              "6  INTELLECTUAL DEVELOPMENTAL DISORDER, X-LINKED ...  \n",
              "7                       SCHAAF-YANG SYNDROME; SHFYNG  \n",
              "8                                   Long QT syndrome  \n",
              "9  BOSCH-BOONSTRA-SCHAAF OPTIC ATROPHY SYNDROME; ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c629088f-1762-4761-af13-c14a1c92b91b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>461</th>\n",
              "      <th>477</th>\n",
              "      <th>479</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hypoplastic left heart syndrome</td>\n",
              "      <td>POIKILODERMA, HEREDITARY FIBROSING, WITH TENDO...</td>\n",
              "      <td>MENTAL RETARDATION, AUTOSOMAL DOMINANT 54; MRD54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DEVELOPMENTAL AND EPILEPTIC ENCEPHALOPATHY 83;...</td>\n",
              "      <td>Rothmund-Thomson syndrome</td>\n",
              "      <td>INTELLECTUAL DEVELOPMENTAL DISORDER, AUTOSOMAL...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MANDIBULOFACIAL DYSOSTOSIS WITH ALOPECIA; MFDA</td>\n",
              "      <td>Blepharocheilodontic syndrome</td>\n",
              "      <td>SMITH-MAGENIS SYNDROME; SMS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LIG4 SYNDROME</td>\n",
              "      <td>Microcephaly, growth restriction and increased...</td>\n",
              "      <td>PHELAN-MCDERMID SYNDROME; PHMDS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SHORT STATURE, BRACHYDACTYLY, IMPAIRED INTELLE...</td>\n",
              "      <td>LIG4 SYNDROME</td>\n",
              "      <td>PITT-HOPKINS SYNDROME; PTHS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NEUROFIBROMATOSIS, TYPE I; NF1</td>\n",
              "      <td>Focal facial dermal dysplasia</td>\n",
              "      <td>HAO-FOUNTAIN SYNDROME; HAFOUS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BOSMA ARHINIA MICROPHTHALMIA SYNDROME; BAMS</td>\n",
              "      <td>BRANCHIOOCULOFACIAL SYNDROME; BOFS</td>\n",
              "      <td>INTELLECTUAL DEVELOPMENTAL DISORDER, X-LINKED ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>INTELLECTUAL DEVELOPMENTAL DISORDER, X-LINKED ...</td>\n",
              "      <td>Hutchinson-Gilford progeria</td>\n",
              "      <td>SCHAAF-YANG SYNDROME; SHFYNG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>MICROCEPHALY, GROWTH DEFICIENCY, SEIZURES, AND...</td>\n",
              "      <td>CDH8</td>\n",
              "      <td>Long QT syndrome</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>RADIO-TARTAGLIA SYNDROME; RATARS</td>\n",
              "      <td>FONTAINE PROGEROID SYNDROME; FPS</td>\n",
              "      <td>BOSCH-BOONSTRA-SCHAAF OPTIC ATROPHY SYNDROME; ...</td>\n",
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