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"nbformat": 4,
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"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"## C01 - Use CSP embeddings\n",
"\n",
"Simple example of how to obtain pretrained CSP embeddings. Read the paper here:[https://arxiv.org/abs/2305.01118](https://arxiv.org/abs/2305.01118). Note that this notebook needs to be run with GPU enabled. To do this got to: \"Runtime -> Change runtime type\""
],
"metadata": {
"id": "ngz8zz9Gvbxh"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tD7wze7andRh",
"outputId": "a2cc59f6-00e8-4c73-b95f-0fe737791593"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into '.'...\n",
"remote: Enumerating objects: 86, done.\u001b[K\n",
"remote: Counting objects: 100% (86/86), done.\u001b[K\n",
"remote: Compressing objects: 100% (69/69), done.\u001b[K\n",
"remote: Total 86 (delta 26), reused 75 (delta 15), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (86/86), 1.58 MiB | 16.53 MiB/s, done.\n",
"Resolving deltas: 100% (26/26), done.\n"
]
}
],
"source": [
"!rm -r sample_data .config # Empty current directory\n",
"!git clone https://github.com/gengchenmai/csp.git . # Clone CSP repository"
]
},
{
"cell_type": "markdown",
"source": [
"Import required packages."
],
"metadata": {
"id": "drQnlZEDwBvA"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"\n",
"import sys\n",
"sys.path.append('./main')\n",
"\n",
"from main.utils import *\n",
"from main.models import *"
],
"metadata": {
"id": "Q72Ypu0Cr3Sc"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Write helper function to load CPS models from checkpoint."
],
"metadata": {
"id": "UUi_LduKwDpA"
}
},
{
"cell_type": "code",
"source": [
"def get_csp(path):\n",
" pretrained_csp = torch.load(path)\n",
"\n",
" params = pretrained_csp['params']\n",
" loc_enc = get_model(\n",
" train_locs = None,\n",
" params = params,\n",
" spa_enc_type = params['spa_enc_type'],\n",
" num_inputs = params['num_loc_feats'],\n",
" num_classes = params['num_classes'],\n",
" num_filts = params['num_filts'],\n",
" num_users = params['num_users'],\n",
" device = params['device'])\n",
"\n",
" model = LocationImageEncoder(loc_enc = loc_enc,\n",
" train_loss = params[\"train_loss\"],\n",
" unsuper_loss = params[\"unsuper_loss\"],\n",
" cnn_feat_dim = params[\"cnn_feat_dim\"],\n",
" spa_enc_type = params[\"spa_enc_type\"]).to(params['device'])\n",
"\n",
" model.load_state_dict(pretrained_csp['state_dict'])\n",
"\n",
" return model"
],
"metadata": {
"id": "eWV6S2SmsX_O"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Download pretrained models. For details see here: [https://gengchenmai.github.io/csp-website/](https://gengchenmai.github.io/csp-website/)"
],
"metadata": {
"id": "IUtwfnVKwNsu"
}
},
{
"cell_type": "code",
"source": [
"!wget -O model_dir.zip 'https://www.dropbox.com/s/qxr644rj1qxekn2/model_dir.zip?dl=1'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3rib-U9ztCCg",
"outputId": "fd0979ce-7bd3-4882-ebfa-ca1c1533a4aa"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2024-01-21 01:57:39-- https://www.dropbox.com/s/qxr644rj1qxekn2/model_dir.zip?dl=1\n",
"Resolving www.dropbox.com (www.dropbox.com)... 162.125.72.18, 2620:100:6021:18::a27d:4112\n",
"Connecting to www.dropbox.com (www.dropbox.com)|162.125.72.18|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: /s/dl/qxr644rj1qxekn2/model_dir.zip [following]\n",
"--2024-01-21 01:57:39-- https://www.dropbox.com/s/dl/qxr644rj1qxekn2/model_dir.zip\n",
"Reusing existing connection to www.dropbox.com:443.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://uc88d2f6a29caa94bff34c8e0b68.dl.dropboxusercontent.com/cd/0/get/CLsPN3FzQu8_q5uGtPvkDlDROOyJ6xA-YyXdunQrn7KAzziriwxL6D5CtJ-GLtERcoOgOxXfm552bAerCPxoiiK73Gcy-_d58UA0Im4LxotstFW-4wLSnulKjrUuv0l_OrxCIyAXp_GP2OHpWKHcgdQN/file?dl=1# [following]\n",
"--2024-01-21 01:57:40-- https://uc88d2f6a29caa94bff34c8e0b68.dl.dropboxusercontent.com/cd/0/get/CLsPN3FzQu8_q5uGtPvkDlDROOyJ6xA-YyXdunQrn7KAzziriwxL6D5CtJ-GLtERcoOgOxXfm552bAerCPxoiiK73Gcy-_d58UA0Im4LxotstFW-4wLSnulKjrUuv0l_OrxCIyAXp_GP2OHpWKHcgdQN/file?dl=1\n",
"Resolving uc88d2f6a29caa94bff34c8e0b68.dl.dropboxusercontent.com (uc88d2f6a29caa94bff34c8e0b68.dl.dropboxusercontent.com)... 162.125.65.15, 2620:100:6021:15::a27d:410f\n",
"Connecting to uc88d2f6a29caa94bff34c8e0b68.dl.dropboxusercontent.com (uc88d2f6a29caa94bff34c8e0b68.dl.dropboxusercontent.com)|162.125.65.15|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 94186705 (90M) [application/binary]\n",
"Saving to: ‘model_dir.zip’\n",
"\n",
"model_dir.zip 100%[===================>] 89.82M 17.4MB/s in 5.5s \n",
"\n",
"2024-01-21 01:57:46 (16.5 MB/s) - ‘model_dir.zip’ saved [94186705/94186705]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!unzip model_dir.zip"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "v3IDc8C9tZZr",
"outputId": "882a9228-af13-4849-adf8-179f055b47ca"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Archive: model_dir.zip\n",
" creating: model_dir/\n",
" inflating: __MACOSX/._model_dir \n",
" inflating: model_dir/.DS_Store \n",
" inflating: __MACOSX/model_dir/._.DS_Store \n",
" creating: model_dir/model_inat_2018/\n",
" inflating: __MACOSX/model_dir/._model_inat_2018 \n",
" creating: model_dir/model_fmow/\n",
" inflating: __MACOSX/model_dir/._model_fmow \n",
" inflating: model_dir/model_inat_2018/.DS_Store \n",
" inflating: __MACOSX/model_dir/model_inat_2018/._.DS_Store \n",
" inflating: model_dir/model_inat_2018/model_inat_2018_gridcell_0.0010_32_0.1000000_1_512_leakyrelu_contsoftmax_ratio0.050_0.000500_1.000_1_1.000_TMP20.0000_1.0000_1.0000.pth.tar \n",
" inflating: __MACOSX/model_dir/model_inat_2018/._model_inat_2018_gridcell_0.0010_32_0.1000000_1_512_leakyrelu_contsoftmax_ratio0.050_0.000500_1.000_1_1.000_TMP20.0000_1.0000_1.0000.pth.tar \n",
" inflating: model_dir/model_inat_2018/model_inat_2018_gridcell_0.0010_32_0.1000000_1_512_leakyrelu_UNSUPER-contsoftmax_0.000500_1.000_1_1.000_TMP20.0000_1.0000_1.0000.pth.tar \n",
" inflating: __MACOSX/model_dir/model_inat_2018/._model_inat_2018_gridcell_0.0010_32_0.1000000_1_512_leakyrelu_UNSUPER-contsoftmax_0.000500_1.000_1_1.000_TMP20.0000_1.0000_1.0000.pth.tar \n",
" inflating: model_dir/model_fmow/.DS_Store \n",
" inflating: __MACOSX/model_dir/model_fmow/._.DS_Store \n",
" inflating: model_dir/model_fmow/model_fmow_gridcell_0.0010_32_0.1000000_1_512_gelu_UNSUPER-contsoftmax_0.000050_1.000_1_0.100_TMP1.0000_1.0000_1.0000.pth.tar \n",
" inflating: __MACOSX/model_dir/model_fmow/._model_fmow_gridcell_0.0010_32_0.1000000_1_512_gelu_UNSUPER-contsoftmax_0.000050_1.000_1_0.100_TMP1.0000_1.0000_1.0000.pth.tar \n",
" inflating: model_dir/model_fmow/model_fmow_gridcell_0.0010_32_0.1000000_1_512_gelu_contsoftmax_ratio0.050_0.000050_1.000_1_0.100_TMP1.0000_1.0000_1.0000.pth.tar \n",
" inflating: __MACOSX/model_dir/model_fmow/._model_fmow_gridcell_0.0010_32_0.1000000_1_512_gelu_contsoftmax_ratio0.050_0.000050_1.000_1_0.100_TMP1.0000_1.0000_1.0000.pth.tar \n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Load CSP model."
],
"metadata": {
"id": "orlY0u8owb7w"
}
},
{
"cell_type": "code",
"source": [
"path = '/content/model_dir/model_fmow/model_fmow_gridcell_0.0010_32_0.1000000_1_512_gelu_UNSUPER-contsoftmax_0.000050_1.000_1_0.100_TMP1.0000_1.0000_1.0000.pth.tar'\n",
"model = get_csp(path)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0HoKFM2atxM2",
"outputId": "3c43c60d-9a5e-4b67-b3bb-75601470fe98"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/content/./main/module.py:98: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.\n",
" nn.init.xavier_uniform(self.linear.weight)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Use CSP model to obtain location embeddings."
],
"metadata": {
"id": "MBWysaAewdpb"
}
},
{
"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.loc_enc(convert_loc_to_tensor(c.numpy()),return_feats=True).detach().cpu()"
],
"metadata": {
"id": "Ku9kf_0su0id"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"emb.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MYtxk8NCvr0M",
"outputId": "33647a0f-772c-4f08-fe51-f77ce7117d26"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"torch.Size([32, 256])"
]
},
"metadata": {},
"execution_count": 8
}
]
}
]
} |