notebooks/C02_Simple_GeoCLIP_Usage.ipynb

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      "source": [
        "## C02 - Use GeoCLIP embeddings\n",
        "\n",
        "Simple example of how to obtain pretrained GeoCLIP embeddings. Read the paper here:[https://arxiv.org/abs/2309.16020](https://arxiv.org/abs/2309.16020). First install the geoclip package (see [https://github.com/VicenteVivan/geo-clip](https://github.com/VicenteVivan/geo-clip))"
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          "text": [
            "Collecting geoclip\n",
            "  Downloading geoclip-1.1.0-py3-none-any.whl (40.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.3/40.3 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from geoclip) (2.1.0+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from geoclip) (0.16.0+cu121)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from geoclip) (9.4.0)\n",
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            "Installing collected packages: geoclip\n",
            "Successfully installed geoclip-1.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install geoclip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the pretrained model directly."
      ],
      "metadata": {
        "id": "nPc2yi39yRje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from geoclip import LocationEncoder\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "model = LocationEncoder()"
      ],
      "metadata": {
        "id": "Q72Ypu0Cr3Sc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obtain GeoCLIP location embeddings."
      ],
      "metadata": {
        "id": "Y8XaPTs6yUu9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c = torch.randn(32, 2)  # Represents a batch of 32 locations (lon/lat)\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "  emb = model(c.flip(1).float()).detach().cpu()"
      ],
      "metadata": {
        "id": "eWV6S2SmsX_O"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emb.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rib-U9ztCCg",
        "outputId": "67cd8c6e-d53d-4317-b5aa-9076df069e96"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([32, 512])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
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