Vits_encoded_image_search.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bwGyeB-w30v"
      },
      "source": [
        "- We will need to install libraries first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "juEyNOxMWHLG",
        "outputId": "3358ebe3-0dba-40da-b823-00b0940c6f02"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx[http2]>=0.14.0->qdrant_client) (3.7.1)\n",
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          ]
        }
      ],
      "source": [
        "!pip install transformers qdrant_client datasets torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ugviIdtJMBX"
      },
      "outputs": [],
      "source": [
        "from transformers import ViTImageProcessor, ViTModel\n",
        "from qdrant_client import QdrantClient\n",
        "from qdrant_client.http import models\n",
        "from datasets import load_dataset, Dataset\n",
        "import numpy as np\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbNS-tnPw-xJ"
      },
      "source": [
        "You can set up databases on you local computer as well but I am going with cloud since my local computer is limited about storage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {
        "id": "pkyRS26WJMBZ"
      },
      "outputs": [],
      "source": [
        "#connect to the vector database\n",
        "qdrant_url = \"\"\n",
        "qdrant_api_key = \"\"\n",
        "\n",
        "client = QdrantClient(\n",
        "    url=qdrant_url,\n",
        "    api_key=qdrant_api_key\n",
        "     )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2agBP2kAxNF0"
      },
      "source": [
        "We are first going to create a collection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 197,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nw-szPR7JMBa",
        "outputId": "5d207cf9-fdb0-4ea4-d3ba-bb0db9f258f0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 197,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "my_collection = \"image_collection\"\n",
        "client.recreate_collection(\n",
        "    collection_name=my_collection,\n",
        "    vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmidJPls7Ziu"
      },
      "source": [
        "- I am going to use Human Action Recognition dataset but you can apply any other dataset just remeber that the mode should be RGB\n",
        "- We are going to add an index column for better seach experience\n",
        "- I got test and train dataset indivudually to test it with unseen data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "metadata": {
        "id": "2muktnO8ch2O"
      },
      "outputs": [],
      "source": [
        "dataset_train = load_dataset(\n",
        "    'Bingsu/Human_Action_Recognition', split='train'\n",
        ")\n",
        "values= list(range(len(dataset_train)))\n",
        "dataset_train=dataset_train.add_column('image_id',values)\n",
        "\n",
        "\n",
        "dataset_test = load_dataset(\n",
        "    'Bingsu/Human_Action_Recognition', split='test'\n",
        ")\n",
        "\n",
        "values_test= list(range(len(dataset_test)))\n",
        "dataset_test=dataset_test.add_column('image_id',values_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 227,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "Gufk6qwmcLf7",
        "outputId": "40b1764a-dd87-4f99-bec6-a6249a269710"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "label of image is : 11\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=240x160>"
            ]
          },
          "execution_count": 227,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(f\"label of image is : {dataset_train[0]['labels']}\")\n",
        "dataset_train[0]['image']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MoNednxsx_6t"
      },
      "source": [
        "- To exctrat embedding from the images, we are going to use pretrained facebook dino-vits16 Vision Transformer model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Nq7Kt91hC5t",
        "outputId": "6a9e7ff3-65e4-4d6f-ae87-e15b841936fa"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of ViTModel were not initialized from the model checkpoint at facebook/dino-vits16 and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "processor = ViTImageProcessor.from_pretrained('facebook/dino-vits16')\n",
        "model = ViTModel.from_pretrained('facebook/dino-vits16').to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kuMC08zszLLy"
      },
      "source": [
        "- Now let's see an example of an embedding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 201,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "itihxJezJMBl",
        "outputId": "01e7e7c9-8e46-42f8-c705-793c7f16e9fa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(torch.Size([1, 3, 224, 224]),\n",
              " {'pixel_values': tensor([[[[2.2318, 2.2318, 2.1975,  ..., 2.1290, 2.1290, 2.1633],\n",
              "           [2.2318, 2.2318, 2.1975,  ..., 2.1290, 2.1290, 2.1462],\n",
              "           [2.2318, 2.2147, 2.1975,  ..., 2.1290, 2.1290, 2.1462],\n",
              "           ...,\n",
              "           [1.9407, 1.9407, 1.9235,  ..., 1.9749, 2.0263, 2.0948],\n",
              "           [1.9578, 1.9578, 1.9407,  ..., 1.9749, 2.0263, 2.0948],\n",
              "           [1.9578, 1.9578, 1.9407,  ..., 1.9749, 2.0263, 2.0948]],\n",
              " \n",
              "          [[2.2535, 2.2535, 2.2185,  ..., 2.0959, 2.1310, 2.1660],\n",
              "           [2.2535, 2.2535, 2.2185,  ..., 2.0959, 2.1310, 2.1485],\n",
              "           [2.2535, 2.2360, 2.2185,  ..., 2.0959, 2.1310, 2.1485],\n",
              "           ...,\n",
              "           [2.1485, 2.1485, 2.1310,  ..., 2.1835, 2.2360, 2.3060],\n",
              "           [2.1660, 2.1660, 2.1485,  ..., 2.1835, 2.2360, 2.3060],\n",
              "           [2.1660, 2.1660, 2.1485,  ..., 2.1835, 2.2360, 2.3060]],\n",
              " \n",
              "          [[2.3786, 2.3786, 2.3437,  ..., 2.3088, 2.3263, 2.3611],\n",
              "           [2.3786, 2.3786, 2.3437,  ..., 2.3088, 2.3263, 2.3437],\n",
              "           [2.3786, 2.3611, 2.3437,  ..., 2.3088, 2.3263, 2.3437],\n",
              "           ...,\n",
              "           [2.3437, 2.3437, 2.3263,  ..., 2.3786, 2.4308, 2.5006],\n",
              "           [2.3611, 2.3611, 2.3437,  ..., 2.3786, 2.4308, 2.5006],\n",
              "           [2.3611, 2.3611, 2.3437,  ..., 2.3786, 2.4308, 2.5006]]]])})"
            ]
          },
          "execution_count": 201,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image = dataset[123][\"image\"]\n",
        "inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
        "inputs['pixel_values'].shape, inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOV41c2JzdOx"
      },
      "source": [
        "- We are going to create payload for to insert along with image embeddings.\n",
        "- This will help us to do search on vector databases with filtering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7UQliw8MJMBm",
        "outputId": "c474c8ba-b97c-446e-96fc-1341521d8cb8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'labels': 11, 'image_id': 0},\n",
              " {'labels': 14, 'image_id': 1},\n",
              " {'labels': 7, 'image_id': 2}]"
            ]
          },
          "execution_count": 193,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "payload = dataset_train.select_columns([\n",
        "    \"labels\", \"image_id\"\n",
        "]).to_pandas().to_dict(orient=\"records\")\n",
        "\n",
        "payload[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eem-Gom68_lq"
      },
      "source": [
        "- Creating embeddings for inserting to DB usong or ViTs model\n",
        "- This process will take some times(30 mins 12k image in my case)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 204,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "6ec2b5777a8a4cf59f0d2076d3a29482",
            "1e8b1cf3e0a34fbeb638dc8f0f3e5480",
            "619f8f9923b74ea1b75f111d7d7ec47a",
            "28f6e9c066614b67abe94e9cf8f977af",
            "aa81d5b6054a47b5a3154067dfff9d56",
            "adf4b305c23a4919aed02acb82d26dd2",
            "7ebf8a7b92994aa389585236ec4b2d8b",
            "3b6067d466024a05aaa0ac3092c54dff",
            "812c1a856bbf4ae28fbbf7d64bfb97f1",
            "24e68a6e2c504c45867357d4807e40eb",
            "81fc337915ef48b8a04c8cc7c3763684"
          ]
        },
        "id": "z0oRKe1ihQAA",
        "outputId": "dcce2748-dd0a-43a2-f7c2-fbfa43ff5457"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ec2b5777a8a4cf59f0d2076d3a29482",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/12600 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['image', 'labels', 'image_id', 'embeddings'],\n",
              "    num_rows: 12600\n",
              "})"
            ]
          },
          "execution_count": 204,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_embeddings(batch):\n",
        "    inputs = processor(images=batch['image'], return_tensors=\"pt\").to(device)\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()\n",
        "    batch['embeddings'] = outputs\n",
        "    return batch\n",
        "\n",
        "dataset_train = dataset_train.map(get_embeddings, batched=True, batch_size=16)\n",
        "dataset_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kEMeowAW0Fla"
      },
      "source": [
        "- Now lets get the embeddings with creating id columns as well"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 205,
      "metadata": {
        "id": "S88uta42JMBm"
      },
      "outputs": [],
      "source": [
        "ids = list(range(dataset_train.num_rows))\n",
        "embeddings = np.array(dataset_train['embeddings'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kLortVM9FCH"
      },
      "source": [
        "- Inserting to db"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 206,
      "metadata": {
        "id": "CTiDg_ZWJMBn"
      },
      "outputs": [],
      "source": [
        "batch_size = 1000\n",
        "\n",
        "for i in range(0, dataset_train.num_rows, batch_size):\n",
        "\n",
        "    low_idx = min(i+batch_size, dataset_train.num_rows)\n",
        "\n",
        "    batch_of_ids = ids[i: low_idx]\n",
        "    batch_of_embs = embeddings[i: low_idx]\n",
        "    batch_of_payloads = payload[i: low_idx]\n",
        "\n",
        "    client.upsert(\n",
        "        collection_name=my_collection,\n",
        "        points=models.Batch(\n",
        "            ids=batch_of_ids,\n",
        "            vectors=batch_of_embs,\n",
        "            payloads=batch_of_payloads\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuiOH0aI0tUJ"
      },
      "source": [
        "- We can check our collection info as follow:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 229,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7oVvxMh0hhk",
        "outputId": "3d1c7d39-66b7-43dc-cd29-4dc910d14042"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('status', <CollectionStatus.GREEN: 'green'>),\n",
              " ('optimizer_status', <OptimizersStatusOneOf.OK: 'ok'>),\n",
              " ('vectors_count', 12600),\n",
              " ('indexed_vectors_count', 0),\n",
              " ('points_count', 12600),\n",
              " ('segments_count', 2),\n",
              " ('config',\n",
              "  CollectionConfig(params=CollectionParams(vectors=VectorParams(size=384, distance=<Distance.COSINE: 'Cosine'>, hnsw_config=None, quantization_config=None, on_disk=None), shard_number=1, replication_factor=1, write_consistency_factor=1, on_disk_payload=True), hnsw_config=HnswConfig(m=16, ef_construct=100, full_scan_threshold=10000, max_indexing_threads=0, on_disk=False, payload_m=None), optimizer_config=OptimizersConfig(deleted_threshold=0.2, vacuum_min_vector_number=1000, default_segment_number=0, max_segment_size=None, memmap_threshold=None, indexing_threshold=20000, flush_interval_sec=5, max_optimization_threads=1), wal_config=WalConfig(wal_capacity_mb=32, wal_segments_ahead=0), quantization_config=None)),\n",
              " ('payload_schema', {})]"
            ]
          },
          "execution_count": 229,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "collection_info = client.get_collection(collection_name='image_collection')\n",
        "list(collection_info)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 261,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "9dNoEISw_7PK",
        "outputId": "2abdbccd-11c3-40a6-9b00-d0706c637827"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "label of this image is 12\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of ViTModel were not initialized from the model checkpoint at facebook/dino-vits16 and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f26dd74d-fe1b-48da-a1fd-c1d1360d69c8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ],
            "text/plain": [
              "          score               label\n",
              "123         1.0            sleeping\n",
              "10574  0.916811  listening_to_music\n",
              "2699   0.909685            sleeping\n",
              "7491   0.909277  listening_to_music\n",
              "5417   0.907981        using_laptop\n",
              "8423   0.907289            sleeping\n",
              "7726   0.905439            sleeping\n",
              "2882   0.904375             hugging\n",
              "10916  0.904311             hugging\n",
              "8354   0.903506        using_laptop"
            ]
          },
          "execution_count": 261,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "label_dict = {0: 'calling',\n",
        " 1: 'clapping',\n",
        " 2: 'cycling',\n",
        " 3: 'dancing',\n",
        " 4: 'drinking',\n",
        " 5: 'eating',\n",
        " 6: 'fighting',\n",
        " 7: 'hugging',\n",
        " 8: 'laughing',\n",
        " 9: 'listening_to_music',\n",
        " 10: 'running',\n",
        " 11: 'sitting',\n",
        " 12: 'sleeping',\n",
        " 13: 'texting',\n",
        " 14: 'using_laptop'}\n",
        "def embed_image(image):\n",
        "  #image = dataset[123][\"image\"]\n",
        "  device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "  processor = ViTImageProcessor.from_pretrained('facebook/dino-vits16')\n",
        "  model = ViTModel.from_pretrained('facebook/dino-vits16').to(device)\n",
        "  inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
        "  embedded_image = model(**inputs).last_hidden_state\n",
        "  return embedded_image\n",
        "\n",
        "def search_image(embedded_image):\n",
        "  results = client.search(\n",
        "    collection_name=my_collection,\n",
        "    query_vector=embedded_image.mean(dim=1)[0].tolist(),\n",
        "    limit=10)\n",
        "  result_dict = {}\n",
        "  for item in results:\n",
        "    score = item.score\n",
        "    label = item.payload['labels']\n",
        "    mapped_label = label_dict.get(label, label)  # Use the mapped label or original label if not found\n",
        "    result_dict[item.id] = {'score': score, 'label': mapped_label}\n",
        "  df = pd.DataFrame(result_dict).T\n",
        "\n",
        "  return df\n",
        "\n",
        "image = dataset_test[47]['image']\n",
        "label = dataset_test[47]['labels']\n",
        "print(f'label of this image is {label}')\n",
        "embedded_image = embed_image(image)\n",
        "results = search_image(embedded_image)\n",
        "results"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
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