File size: 12,904 Bytes
eb1aec4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"## A01 - Simple usage of pretrained SatCLIP embeddings\n",
"\n",
"To obtained pretrained **SatCLIP** embeddings, first install the repository."
],
"metadata": {
"id": "ngz8zz9Gvbxh"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tD7wze7andRh",
"outputId": "a8f55f89-e313-4792-c34b-c33026ed2dc0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into '.'...\n",
"remote: Enumerating objects: 131, done.\u001b[K\n",
"remote: Counting objects: 100% (36/36), done.\u001b[K\n",
"remote: Compressing objects: 100% (32/32), done.\u001b[K\n",
"remote: Total 131 (delta 14), reused 16 (delta 4), pack-reused 95\u001b[K\n",
"Receiving objects: 100% (131/131), 9.01 MiB | 21.25 MiB/s, done.\n",
"Resolving deltas: 100% (48/48), done.\n"
]
}
],
"source": [
"!rm -r sample_data .config # Empty current directory\n",
"!git clone https://github.com/microsoft/satclip.git . # Clone SatCLIP repository"
]
},
{
"cell_type": "markdown",
"source": [
"Now install the required Python packages."
],
"metadata": {
"id": "hOEZnNt1v2Bl"
}
},
{
"cell_type": "code",
"source": [
"!pip install lightning --quiet\n",
"!pip install rasterio --quiet\n",
"!pip install torchgeo --quiet"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q72Ypu0Cr3Sc",
"outputId": "d54f24b8-26b1-4f86-ffc8-23da0cc63710"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m806.1/806.1 kB\u001b[0m \u001b[31m40.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m776.9/776.9 kB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.6/20.6 MB\u001b[0m \u001b[31m27.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.5/378.5 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m705.7/705.7 kB\u001b[0m \u001b[31m37.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m670.7/670.7 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m488.6/488.6 kB\u001b[0m \u001b[31m41.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.7/106.7 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m66.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.5/154.5 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.6/137.6 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m189.7/189.7 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.7/101.7 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m594.2/594.2 kB\u001b[0m \u001b[31m44.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"You can find a list of pretrained SatCLIP models [here](https://github.com/microsoft/satclip#pretrained-models). Let's download a SatCLIP using a ResNet18 vision encoder and $L=10$ Legendre polynomials for spherical harmonics calculation in the location encoder."
],
"metadata": {
"id": "pmcZKqU9wOTk"
}
},
{
"cell_type": "code",
"source": [
"!wget 'https://satclip.z13.web.core.windows.net/satclip/satclip-resnet18-l10.ckpt'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "N4S7_2fqqWF1",
"outputId": "6875c479-e70e-488f-bb1d-ccbca0105ba1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-12-07 14:55:28-- https://satclip.z13.web.core.windows.net/satclip/satclip-resnet18-l10.ckpt\n",
"Resolving satclip.z13.web.core.windows.net (satclip.z13.web.core.windows.net)... 52.239.221.231\n",
"Connecting to satclip.z13.web.core.windows.net (satclip.z13.web.core.windows.net)|52.239.221.231|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 57201927 (55M) [application/zip]\n",
"Saving to: ‘satclip-resnet18-l10.ckpt’\n",
"\n",
"satclip-resnet18-l1 100%[===================>] 54.55M 91.7MB/s in 0.6s \n",
"\n",
"2023-12-07 14:55:29 (91.7 MB/s) - ‘satclip-resnet18-l10.ckpt’ saved [57201927/57201927]\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Load required packages."
],
"metadata": {
"id": "P_FUpXihwwpB"
}
},
{
"cell_type": "code",
"source": [
"import sys\n",
"sys.path.append('./satclip')\n",
"\n",
"import torch\n",
"from load import get_satclip"
],
"metadata": {
"id": "grEIwoFjoHvu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"And now we can sample some random lat/lon locations for which we obtain SatCLIP embeddings."
],
"metadata": {
"id": "mXdw7j9cw1sK"
}
},
{
"cell_type": "code",
"source": [
"satclip_path = 'satclip-resnet18-l10.ckpt'\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"c = torch.randn(32, 2) # Represents a batch of 32 locations (lon/lat)\n",
"\n",
"model = get_satclip(satclip_path, device=device) # Only loads location encoder by default\n",
"model.eval()\n",
"with torch.no_grad():\n",
" emb = model(c.double().to(device)).detach().cpu()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_oOATwQHn5Pc",
"outputId": "97178d9f-adeb-41c5-aa21-7ae22439caa4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"using pretrained moco resnet18\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Downloading: \"https://huggingface.co/torchgeo/resnet18_sentinel2_all_moco/resolve/main/resnet18_sentinel2_all_moco-59bfdff9.pth\" to /root/.cache/torch/hub/checkpoints/resnet18_sentinel2_all_moco-59bfdff9.pth\n",
"100%|██████████| 42.8M/42.8M [00:00<00:00, 59.4MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"emb.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DY-yS8cNru7w",
"outputId": "5efd6439-3f3a-47f3-f50c-707224069b04"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"torch.Size([32, 256])"
]
},
"metadata": {},
"execution_count": 6
}
]
}
]
} |