MetaCLIP/MetaCLIP_clustering.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Clustering with MetaCLIP"
      ],
      "metadata": {
        "id": "mFWC2VWkV7Zl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installing dependencies"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aWwu4E8gV5A_"
      },
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      "source": [
        "! pip install transformers torch faiss-gpu datasets matplotlib scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the model"
      ],
      "metadata": {
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      "source": [
        "import torch\n",
        "from PIL import Image\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoProcessor, AutoModelForZeroShotImageClassification, AutoTokenizer\n",
        "from tqdm import tqdm\n",
        "#Only needed in Google colab\n",
        "import torch._dynamo\n",
        "torch._dynamo.config.suppress_errors = True\n",
        "import faiss\n",
        "import numpy as np\n",
        "\n",
        "#Define device (GPU or CPU)\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "#Load CLIP model, processor and tokenizer\n",
        "processor = AutoProcessor.from_pretrained(\"facebook/metaclip-b16-fullcc2.5b\")\n",
        "model = AutoModelForZeroShotImageClassification.from_pretrained(\"facebook/metaclip-b16-fullcc2.5b\",  torch_dtype=torch.float16).to(device)\n",
        "model = torch.compile(model)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"facebook/metaclip-b16-fullcc2.5b\")"
      ],
      "metadata": {
        "id": "RNbYU1dmWFzk"
      },
      "execution_count": null,
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    {
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      "source": [
        "## Loading the dataset"
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        "#Load cifar10 dataset\n",
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        "#Display an image\n",
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              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
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            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extracting the features"
      ],
      "metadata": {
        "id": "dXZz_R0kWO-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Add a vector to FAISS index\n",
        "def add_vector_to_index(embedding, index):\n",
        "    #convert embedding to numpy\n",
        "    vector = embedding.detach().cpu().numpy()\n",
        "    #Convert to float32 numpy\n",
        "    vector = np.float32(vector)\n",
        "    #Normalize vector\n",
        "    faiss.normalize_L2(vector)\n",
        "    #Add to index\n",
        "    index.add(vector)\n",
        "\n",
        "#Extract features of a given image\n",
        "def extract_features_clip(image):\n",
        "    with torch.no_grad():\n",
        "        inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
        "        image_features = model.get_image_features(**inputs)\n",
        "        return image_features\n",
        "\n",
        "#Create FAISS index\n",
        "index = faiss.IndexFlatL2(512)\n",
        "\n",
        "#Process the dataset to extract all features and store in index\n",
        "for image in tqdm(dataset['test']):\n",
        "    clip_features = extract_features_clip(image['img'])\n",
        "    add_vector_to_index(clip_features,index)\n",
        "\n",
        "#Write index locally. Not needed after but can be useful for future retrieval\n",
        "faiss.write_index(index,\"clip.index\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFLAxH9UWShs",
        "outputId": "5d4c18b5-c3a4-4b3c-a8ed-da89a70f2a36"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10000/10000 [02:53<00:00, 57.65it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating 10 clusters"
      ],
      "metadata": {
        "id": "afBh29pRWV4T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Retrieve the vectors\n",
        "vectors = index.reconstruct_n(0, 10000)\n",
        "\n",
        "#Define clusters and parameters\n",
        "x= vectors\n",
        "ncentroids = 10\n",
        "niter = 50\n",
        "verbose = True\n",
        "d = x.shape[1]\n",
        "\n",
        "import time\n",
        "t0=time.time()\n",
        "#Launch clustering\n",
        "kmeans = faiss.Kmeans(d, ncentroids, niter=niter, verbose=verbose)\n",
        "kmeans.train(x)\n",
        "print('Clustering done in ', time.time()-t0)\n",
        "\n",
        "#cluster assignments: [array of vectors, array of indices (cluster assigned to a given vector)]\n",
        "cluster_assignments = kmeans.assign(vectors)\n",
        "print(cluster_assignments)\n",
        "#Print indices only\n",
        "clusters_ind = cluster_assignments[1]\n",
        "print(clusters_ind)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seinpujyWXkn",
        "outputId": "8c034697-b2ca-482c-cc7f-d1a4102c1442"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clustering done in  0.3226318359375\n",
            "(array([0.12448764, 0.16233206, 0.15056908, ..., 0.13251746, 0.24733269,\n",
            "       0.14068532], dtype=float32), array([4, 8, 8, ..., 6, 7, 2]))\n",
            "[4 8 8 ... 6 7 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating the distribution matrix"
      ],
      "metadata": {
        "id": "t8iLKkJ5Wcdj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "distribution = np.zeros((10, 10), dtype=int)\n",
        "\n",
        "for vector_index,cluster_nb in enumerate(clusters_ind):\n",
        "    label = dataset['test'][vector_index]['label']\n",
        "    distribution[cluster_nb][label]+=1\n",
        "\n",
        "print(distribution)\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=distribution)\n",
        "disp.plot()\n",
        "plt.ylabel('Cluster ID')\n",
        "plt.xlabel('Classes')\n",
        "labels = dataset[\"train\"].features[\"label\"].names\n",
        "x = range(len(labels))\n",
        "ticks = labels\n",
        "plt.xticks(x, ticks)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "L6xg9PvuWezb",
        "outputId": "828c8e54-03a0-4b70-d2c3-0f45eccc8170"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0   0   0  35   1 697   1   0   0   0]\n",
            " [  0   0 140  11 869   2   0  13   0   0]\n",
            " [  0   0   0   1   8   4   0 920   0   0]\n",
            " [939   0   3   0   0   0   0   0   9   0]\n",
            " [ 52   6 291  86 107  40 182  59  32   9]\n",
            " [  0   0 555   0   5   0   4   0   0   0]\n",
            " [  0   0   8 853   4 256  12   8   0   0]\n",
            " [  1 994   0   2   0   0   0   0   0 988]\n",
            " [  8   0   1   0   0   0   0   0 959   3]\n",
            " [  0   0   2  12   6   1 801   0   0   0]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}