ReverseSearchEngine.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Reverse Search Engine\n",
        "Given an image,the goal is to retrieve the top-k similar images from a database at speed."
      ],
      "metadata": {
        "id": "ToDGK52Kreo1"
      }
    },
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      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
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            "text/plain": [
              "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
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      ],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.models as models\n",
        "from PIL import Image\n",
        "\n",
        "# Load the pretrained model\n",
        "model = models.resnet18(pretrained=True)\n",
        "\n",
        "# Use the model object to select the desired layer\n",
        "layer = model._modules.get('avgpool')\n",
        "\n",
        "# Set model to evaluation mode\n",
        "model.eval()\n",
        "\n",
        "transforms = torchvision.transforms.Compose([\n",
        "    torchvision.transforms.Resize(256),\n",
        "    torchvision.transforms.CenterCrop(224),\n",
        "    torchvision.transforms.ToTensor(),\n",
        "    torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])\n",
        "\n",
        "\n",
        "def get_vector(image):\n",
        "    # Create a PyTorch tensor with the transformed image\n",
        "    t_img = transforms(image)\n",
        "    # Create a vector of zeros that will hold our feature vector\n",
        "    # The 'avgpool' layer has an output size of 512\n",
        "    my_embedding = torch.zeros(512)\n",
        "\n",
        "    # Define a function that will copy the output of a layer\n",
        "    def copy_data(m, i, o):\n",
        "        my_embedding.copy_(o.flatten())                 # <-- flatten\n",
        "\n",
        "    # Attach that function to our selected layer\n",
        "    h = layer.register_forward_hook(copy_data)\n",
        "    # Run the model on our transformed image\n",
        "    with torch.no_grad():                               # <-- no_grad context\n",
        "        model(t_img.unsqueeze(0))                       # <-- unsqueeze\n",
        "    # Detach our copy function from the layer\n",
        "    h.remove()\n",
        "    # Return the feature vector\n",
        "    return my_embedding"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dRp5g7bu5yW",
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      "execution_count": null,
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('data.csv')\n",
        "df['article_id_path'] = df['article_id'].apply(lambda x: '0' + str(x) + '.jpg')"
      ],
      "metadata": {
        "id": "oycDIEETu8Rn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "def get_vector_for_image(article_id_path,product_type_name):\n",
        "  img_path = os.path.join('/content/drive/MyDrive/data/',product_type_name,article_id_path)\n",
        "  img = Image.open(img_path)\n",
        "  vector = get_vector(img)\n",
        "  return vector\n",
        "  \n",
        "df['Embeddings'] = df.apply(lambda x: get_vector_for_image(x.article_id_path,x.product_type_name), axis = 1)"
      ],
      "metadata": {
        "id": "EUuKBZD60S8s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save as a pickle for future processing\n",
        "df.to_pickle('Embeddings-1000.pkl')"
      ],
      "metadata": {
        "id": "vbCJ4i5z5IPb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install annoy"
      ],
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        "id": "CQ3nfEgZ5goN",
        "outputId": "ee58e002-5b1d-454c-95cf-7e33716e22ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting annoy\n",
            "  Downloading annoy-1.17.0.tar.gz (646 kB)\n",
            "\u001b[K     |████████████████████████████████| 646 kB 5.3 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: annoy\n",
            "  Building wheel for annoy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for annoy: filename=annoy-1.17.0-cp37-cp37m-linux_x86_64.whl size=391576 sha256=ddd8d0a91ba9e05a546e2a96a19b5dac741f0093d76fd523fbbb095ca56de729\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/e8/1e/7cc9ebbfa87a3b9f8ba79408d4d31831d67eea918b679a4c07\n",
            "Successfully built annoy\n",
            "Installing collected packages: annoy\n",
            "Successfully installed annoy-1.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from annoy import AnnoyIndex\n",
        "f = len(df['Embeddings'][0])\n",
        "t = AnnoyIndex(f, metric='euclidean')\n",
        "\n",
        "ntree = 500 # hyper-parameter, the more the number of trees better the prediction\n",
        "for i, vector in enumerate(df['Embeddings']):\n",
        "    t.add_item(i, vector)\n",
        "_  = t.build(ntree)"
      ],
      "metadata": {
        "id": "OYhA9V2w5tpw"
      },
      "execution_count": null,
      "outputs": []
    },
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        "df"
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      "execution_count": null,
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        {
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            "text/plain": [
              "     article_id  product_code               prod_name  product_type_no  \\\n",
              "0     625189002        625189   SB Sebastian rain jkt              262   \n",
              "1     801905013        801905  Tom Chino Shorts (TVP)              274   \n",
              "2     637922006        637922       Brian Tuxedo Coat              262   \n",
              "3     698111001        698111           Solene shorts              274   \n",
              "4     636469001        636469          kourtny shorts              274   \n",
              "..          ...           ...                     ...              ...   \n",
              "995   729948001        729948         OC Rachel Skirt              275   \n",
              "996   791445002        791445             Fargo tunic              258   \n",
              "997   658459001        658459         Jogger lined 79              272   \n",
              "998   741417001        741417        SPEED Kasuma 3-p              272   \n",
              "999   728156014        728156            Robin blazer              264   \n",
              "\n",
              "    product_type_name  product_group_name  graphical_appearance_no  \\\n",
              "0              Jacket  Garment Upper body                  1010016   \n",
              "1              Shorts  Garment Lower body                  1010001   \n",
              "2              Jacket  Garment Upper body                  1010016   \n",
              "3              Shorts  Garment Lower body                  1010018   \n",
              "4              Shorts  Garment Lower body                  1010016   \n",
              "..                ...                 ...                      ...   \n",
              "995             Skirt  Garment Lower body                  1010016   \n",
              "996            Blouse  Garment Upper body                  1010016   \n",
              "997          Trousers  Garment Lower body                  1010023   \n",
              "998          Trousers  Garment Lower body                  1010016   \n",
              "999            Blazer  Garment Upper body                  1010016   \n",
              "\n",
              "    graphical_appearance_name  colour_group_code colour_group_name  ...  \\\n",
              "0                       Solid                 73         Dark Blue  ...   \n",
              "1            All over pattern                 13             Beige  ...   \n",
              "2                       Solid                  9             Black  ...   \n",
              "3                   Treatment                 71        Light Blue  ...   \n",
              "4                       Solid                  9             Black  ...   \n",
              "..                        ...                ...               ...  ...   \n",
              "995                     Solid                 71        Light Blue  ...   \n",
              "996                     Solid                 31      Light Orange  ...   \n",
              "997                     Denim                 73         Dark Blue  ...   \n",
              "998                     Solid                 73         Dark Blue  ...   \n",
              "999                     Solid                 51        Light Pink  ...   \n",
              "\n",
              "                 index_name index_group_no  index_group_name section_no  \\\n",
              "0     Children Sizes 92-140              4     Baby/Children         45   \n",
              "1     Children Sizes 92-140              4     Baby/Children         46   \n",
              "2                Ladieswear              1        Ladieswear         19   \n",
              "3    Children Sizes 134-170              4     Baby/Children         77   \n",
              "4                   Divided              2           Divided         53   \n",
              "..                      ...            ...               ...        ...   \n",
              "995                 Divided              2           Divided         50   \n",
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              "997   Children Sizes 92-140              4     Baby/Children         46   \n",
              "998        Baby Sizes 50-98              4     Baby/Children         41   \n",
              "999              Ladieswear              1        Ladieswear         11   \n",
              "\n",
              "           section_name garment_group_no             garment_group_name  \\\n",
              "0        Kids Outerwear             1007                        Outdoor   \n",
              "1              Kids Boy             1025                         Shorts   \n",
              "2        Womens Jackets             1007                        Outdoor   \n",
              "3            Young Girl             1005                   Jersey Fancy   \n",
              "4    Divided Collection             1025                         Shorts   \n",
              "..                  ...              ...                            ...   \n",
              "995    Divided Projects             1009                       Trousers   \n",
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              "997            Kids Boy             1016                 Trousers Denim   \n",
              "998            Baby Boy             1006  Woven/Jersey/Knitted mix Baby   \n",
              "999    Womens Tailoring             1008                        Dressed   \n",
              "\n",
              "                                           detail_desc  article_id_path  \\\n",
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              "3    Shorts in stretch jersey with a denim look mad...   0698111001.jpg   \n",
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              "..                                                 ...              ...   \n",
              "995  Knee-length skirt in a cotton weave with brode...   0729948001.jpg   \n",
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              "997  Joggers in washed denim with an elasticated dr...   0658459001.jpg   \n",
              "998  Trousers in cotton twill with an adjustable el...   0741417001.jpg   \n",
              "999  Straight-cut jacket in woven fabric with a sha...   0728156014.jpg   \n",
              "\n",
              "                                            Embeddings  \n",
              "0    [tensor(0.9268), tensor(2.1596), tensor(0.3360...  \n",
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              "..                                                 ...  \n",
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              "      <td>625189002</td>\n",
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              "      <td>SB Sebastian rain jkt</td>\n",
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              "      <td>637922006</td>\n",
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              "      <td>Brian Tuxedo Coat</td>\n",
              "      <td>262</td>\n",
              "      <td>Jacket</td>\n",
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              "      <td>1010016</td>\n",
              "      <td>Solid</td>\n",
              "      <td>9</td>\n",
              "      <td>Black</td>\n",
              "      <td>...</td>\n",
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              "      <td>1</td>\n",
              "      <td>Ladieswear</td>\n",
              "      <td>19</td>\n",
              "      <td>Womens Jackets</td>\n",
              "      <td>1007</td>\n",
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              "      <td>Fitted coat in woven fabric with a tuxedo-styl...</td>\n",
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              "      <td>[tensor(0.3852), tensor(0.3776), tensor(0.1682...</td>\n",
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              "      <th>3</th>\n",
              "      <td>698111001</td>\n",
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              "      <td>Solene shorts</td>\n",
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              "      <td>1010018</td>\n",
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              "      <td>71</td>\n",
              "      <td>Light Blue</td>\n",
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              "      <td>Children Sizes 134-170</td>\n",
              "      <td>4</td>\n",
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              "      <td>77</td>\n",
              "      <td>Young Girl</td>\n",
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              "    <tr>\n",
              "      <th>996</th>\n",
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              "      <td>1016</td>\n",
              "      <td>Trousers Denim</td>\n",
              "      <td>Joggers in washed denim with an elasticated dr...</td>\n",
              "      <td>0658459001.jpg</td>\n",
              "      <td>[tensor(0.3145), tensor(1.6253), tensor(0.0438...</td>\n",
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              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>741417001</td>\n",
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              "      <td>1010016</td>\n",
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              "      <td>Dark Blue</td>\n",
              "      <td>...</td>\n",
              "      <td>Baby Sizes 50-98</td>\n",
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              "      <td>Baby/Children</td>\n",
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              "      <td>1006</td>\n",
              "      <td>Woven/Jersey/Knitted mix Baby</td>\n",
              "      <td>Trousers in cotton twill with an adjustable el...</td>\n",
              "      <td>0741417001.jpg</td>\n",
              "      <td>[tensor(0.0369), tensor(0.2694), tensor(0.2917...</td>\n",
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              "    <tr>\n",
              "      <th>999</th>\n",
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              "      <td>Garment Upper body</td>\n",
              "      <td>1010016</td>\n",
              "      <td>Solid</td>\n",
              "      <td>51</td>\n",
              "      <td>Light Pink</td>\n",
              "      <td>...</td>\n",
              "      <td>Ladieswear</td>\n",
              "      <td>1</td>\n",
              "      <td>Ladieswear</td>\n",
              "      <td>11</td>\n",
              "      <td>Womens Tailoring</td>\n",
              "      <td>1008</td>\n",
              "      <td>Dressed</td>\n",
              "      <td>Straight-cut jacket in woven fabric with a sha...</td>\n",
              "      <td>0728156014.jpg</td>\n",
              "      <td>[tensor(1.3273), tensor(1.2449), tensor(0.1245...</td>\n",
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              "<p>1000 rows × 27 columns</p>\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "def get_similar_images_annoy(img_index):\n",
        "    start = time.time()\n",
        "    base_img_id, base_vector, base_label  = df.iloc[img_index, [0,-1,4]]\n",
        "    similar_img_ids = t.get_nns_by_item(img_index, 5)\n",
        "    end = time.time()\n",
        "    print(f'{(end - start) * 1000} ms')\n",
        "    return base_img_id, base_label, df.iloc[similar_img_ids]"
      ],
      "metadata": {
        "id": "_ndJ4tN06pRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_image, base_label, similar_images_df = get_similar_images_annoy(0)"
      ],
      "metadata": {
        "colab": {
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        },
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              "     article_id  product_code              prod_name  product_type_no  \\\n",
              "0     625189002        625189  SB Sebastian rain jkt              262   \n",
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              "981   803662001        803662        CARTER PU SHIRT              259   \n",
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              "0              Jacket  Garment Upper body                  1010016   \n",
              "870            Jacket  Garment Upper body                  1010016   \n",
              "981             Shirt  Garment Upper body                  1010016   \n",
              "639            Hoodie  Garment Upper body                  1010016   \n",
              "323            Hoodie  Garment Upper body                  1010016   \n",
              "\n",
              "    graphical_appearance_name  colour_group_code colour_group_name  ...  \\\n",
              "0                       Solid                 73         Dark Blue  ...   \n",
              "870                     Solid                  9             Black  ...   \n",
              "981                     Solid                  9             Black  ...   \n",
              "639                     Solid                  9             Black  ...   \n",
              "323                     Solid                  9             Black  ...   \n",
              "\n",
              "                 index_name index_group_no  index_group_name section_no  \\\n",
              "0     Children Sizes 92-140              4     Baby/Children         45   \n",
              "870  Children Sizes 134-170              4     Baby/Children         45   \n",
              "981                 Divided              2           Divided         53   \n",
              "639                Menswear              3          Menswear         31   \n",
              "323                Menswear              3          Menswear         26   \n",
              "\n",
              "           section_name garment_group_no garment_group_name  \\\n",
              "0        Kids Outerwear             1007            Outdoor   \n",
              "870      Kids Outerwear             1007            Outdoor   \n",
              "981  Divided Collection             1010            Blouses   \n",
              "639      Mens Outerwear             1007            Outdoor   \n",
              "323       Men Underwear             1002       Jersey Basic   \n",
              "\n",
              "                                           detail_desc  article_id_path  \\\n",
              "0    Rain jacket in windproof, waterproof functiona...   0625189002.jpg   \n",
              "870  Baseball jacket in woven fabric with a sheen. ...   0564472001.jpg   \n",
              "981  Straight-cut shirt in imitation leather with a...   0803662001.jpg   \n",
              "639  Windbreaker in woven fabric with a mesh-lined,...   0477163001.jpg   \n",
              "323  Top in sweatshirt fabric with a lined drawstri...   0569974001.jpg   \n",
              "\n",
              "                                            Embeddings  \n",
              "0    [tensor(0.9268), tensor(2.1596), tensor(0.3360...  \n",
              "870  [tensor(1.0939), tensor(1.4100), tensor(0.1328...  \n",
              "981  [tensor(0.9985), tensor(2.7406), tensor(0.4034...  \n",
              "639  [tensor(0.7147), tensor(2.5043), tensor(0.4261...  \n",
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}