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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"## C03 - Use GPS2Vec embeddings\n",
"\n",
"Simple example of how to obtain pretrained GPS2Vec embeddings. Read the paper here:[https://ieeexplore.ieee.org/abstract/document/9360464?casa_token=N4drK3PtmXMAAAAA:c096iwtxjU271IVjZePsFCH8Xm-7RWl7JztS-QxLgVKIo5ayltwAjzdEXXLb7xcyQKNyOvvN](https://ieeexplore.ieee.org/abstract/document/9360464?casa_token=N4drK3PtmXMAAAAA:c096iwtxjU271IVjZePsFCH8Xm-7RWl7JztS-QxLgVKIo5ayltwAjzdEXXLb7xcyQKNyOvvN).\n",
"\n",
"First install needed packages."
],
"metadata": {
"id": "ngz8zz9Gvbxh"
}
},
{
"cell_type": "code",
"source": [
"!rm -r sample_data .config # Empty current directory\n",
"!git clone https://github.com/yifangyin/GPS2Vec.git . # Clone GPS2Vec repository"
],
"metadata": {
"id": "I4-8JQhl0ntG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install utm"
],
"metadata": {
"id": "_y16t1ho04qm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Download pretrained models. For more details, see here: [https://github.com/yifangyin/GPS2Vec](https://github.com/yifangyin/GPS2Vec)"
],
"metadata": {
"id": "KLmByT0cB1Y_"
}
},
{
"cell_type": "code",
"source": [
"!wget -O models_tag.zip 'https://www.dropbox.com/s/j8b4h3ynkv42gj4/models_tag.zip?dl=1'\n",
"!wget -O models_visual.zip 'https://www.dropbox.com/s/kcsadz2fl6ynymh/models_visual.zip?dl=1'"
],
"metadata": {
"id": "Op4IY-t-2QO3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!unzip -q models_tag.zip\n",
"!unzip -q models_visual.zip"
],
"metadata": {
"id": "GXd2WAYt2fsb"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Write helper function."
],
"metadata": {
"id": "VWhjuK-tB5lV"
}
},
{
"cell_type": "code",
"source": [
"from gps2vec import *\n",
"import torch\n",
"import numpy as np"
],
"metadata": {
"id": "e7yzSifX01l4"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def get_gps2vec(locations,basedir,model='visual'):\n",
" nrows = 20\n",
" ncols = 20\n",
" sigma = 20000\n",
" if model=='visual':\n",
" modeldir=basedir+\"/models_visual\"\n",
" flag = 0\n",
" elif model=='tag':\n",
" modeldir=basedir+\"/models_tag\"\n",
" flag = 1\n",
" else:\n",
" raise ValueError('Invalid model')\n",
" out = []\n",
" for location in locations:\n",
" geofea = georep(location,modeldir,nrows,ncols,sigma,flag)\n",
" out.append(np.asarray(geofea))\n",
" return np.asarray(out, dtype=object)"
],
"metadata": {
"id": "MYtxk8NCvr0M"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Obtain location embeddings."
],
"metadata": {
"id": "Jv3GRaaIB7EJ"
}
},
{
"cell_type": "code",
"source": [
"c = torch.Tensor([[-74.0060, 40.7128], [-118.2437, 34.0522]]) # Represents a batch of 2 locations (lon/lat)\n",
"\n",
"emb = get_gps2vec(np.flip(c.numpy(),1),'',model='visual')"
],
"metadata": {
"id": "l7xHob8y1OSM"
},
"execution_count": null,
"outputs": []
}
]
} |