DINOv3
Browse files- DINOv3_FT.ipynb +1774 -0
DINOv3_FT.ipynb
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|
| 1 |
+
{
|
| 2 |
+
"nbformat": 4,
|
| 3 |
+
"nbformat_minor": 0,
|
| 4 |
+
"metadata": {
|
| 5 |
+
"colab": {
|
| 6 |
+
"provenance": [],
|
| 7 |
+
"machine_shape": "hm",
|
| 8 |
+
"gpuType": "L4"
|
| 9 |
+
},
|
| 10 |
+
"kernelspec": {
|
| 11 |
+
"name": "python3",
|
| 12 |
+
"display_name": "Python 3"
|
| 13 |
+
},
|
| 14 |
+
"language_info": {
|
| 15 |
+
"name": "python"
|
| 16 |
+
},
|
| 17 |
+
"accelerator": "GPU",
|
| 18 |
+
"widgets": {
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" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m838.5/838.5 kB\u001b[0m \u001b[31m41.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
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"## Dataset"
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"id": "5AJ3YVCE8S9Y"
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"We will do a very small run on food101 dataset."
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"id": "s_Aabbb6VBZt"
|
| 1102 |
+
}
|
| 1103 |
+
},
|
| 1104 |
+
{
|
| 1105 |
+
"cell_type": "code",
|
| 1106 |
+
"source": [
|
| 1107 |
+
"from datasets import load_dataset\n",
|
| 1108 |
+
"\n",
|
| 1109 |
+
"ds = load_dataset(\"ethz/food101\")\n",
|
| 1110 |
+
"\n",
|
| 1111 |
+
"train_ds = ds[\"train\"]\n",
|
| 1112 |
+
"train_ds = train_ds.shuffle().train_test_split(test_size=0.9)[\"train\"]\n",
|
| 1113 |
+
"val_ds = ds[\"validation\"].shuffle().train_test_split(test_size=0.9)[\"train\"]"
|
| 1114 |
+
],
|
| 1115 |
+
"metadata": {
|
| 1116 |
+
"id": "Cxzbngbq4K31"
|
| 1117 |
+
},
|
| 1118 |
+
"execution_count": 7,
|
| 1119 |
+
"outputs": []
|
| 1120 |
+
},
|
| 1121 |
+
{
|
| 1122 |
+
"cell_type": "code",
|
| 1123 |
+
"source": [
|
| 1124 |
+
"train_ds"
|
| 1125 |
+
],
|
| 1126 |
+
"metadata": {
|
| 1127 |
+
"colab": {
|
| 1128 |
+
"base_uri": "https://localhost:8080/"
|
| 1129 |
+
},
|
| 1130 |
+
"id": "g1wl86sp8L6C",
|
| 1131 |
+
"outputId": "1b42f43f-df62-4eba-f469-54cabd232cf9"
|
| 1132 |
+
},
|
| 1133 |
+
"execution_count": 8,
|
| 1134 |
+
"outputs": [
|
| 1135 |
+
{
|
| 1136 |
+
"output_type": "execute_result",
|
| 1137 |
+
"data": {
|
| 1138 |
+
"text/plain": [
|
| 1139 |
+
"Dataset({\n",
|
| 1140 |
+
" features: ['image', 'label'],\n",
|
| 1141 |
+
" num_rows: 7575\n",
|
| 1142 |
+
"})"
|
| 1143 |
+
]
|
| 1144 |
+
},
|
| 1145 |
+
"metadata": {},
|
| 1146 |
+
"execution_count": 8
|
| 1147 |
+
}
|
| 1148 |
+
]
|
| 1149 |
+
},
|
| 1150 |
+
{
|
| 1151 |
+
"cell_type": "code",
|
| 1152 |
+
"source": [
|
| 1153 |
+
"val_ds"
|
| 1154 |
+
],
|
| 1155 |
+
"metadata": {
|
| 1156 |
+
"colab": {
|
| 1157 |
+
"base_uri": "https://localhost:8080/"
|
| 1158 |
+
},
|
| 1159 |
+
"id": "Tq5OiKxvVj9k",
|
| 1160 |
+
"outputId": "391489ba-d95f-498a-b4bb-f959e19686b0"
|
| 1161 |
+
},
|
| 1162 |
+
"execution_count": 9,
|
| 1163 |
+
"outputs": [
|
| 1164 |
+
{
|
| 1165 |
+
"output_type": "execute_result",
|
| 1166 |
+
"data": {
|
| 1167 |
+
"text/plain": [
|
| 1168 |
+
"Dataset({\n",
|
| 1169 |
+
" features: ['image', 'label'],\n",
|
| 1170 |
+
" num_rows: 2525\n",
|
| 1171 |
+
"})"
|
| 1172 |
+
]
|
| 1173 |
+
},
|
| 1174 |
+
"metadata": {},
|
| 1175 |
+
"execution_count": 9
|
| 1176 |
+
}
|
| 1177 |
+
]
|
| 1178 |
+
},
|
| 1179 |
+
{
|
| 1180 |
+
"cell_type": "code",
|
| 1181 |
+
"source": [
|
| 1182 |
+
"num_classes = train_ds.features[\"label\"].num_classes\n",
|
| 1183 |
+
"id2label = {i: name for i, name in enumerate(train_ds.features[\"label\"].names)}\n",
|
| 1184 |
+
"label2id = {v: k for k, v in id2label.items()}\n",
|
| 1185 |
+
"print(f\"Classes: {num_classes}\")"
|
| 1186 |
+
],
|
| 1187 |
+
"metadata": {
|
| 1188 |
+
"colab": {
|
| 1189 |
+
"base_uri": "https://localhost:8080/"
|
| 1190 |
+
},
|
| 1191 |
+
"id": "1JcvDPFK8Scd",
|
| 1192 |
+
"outputId": "5c920e23-e96b-4c62-bf3a-7db183c97f48"
|
| 1193 |
+
},
|
| 1194 |
+
"execution_count": 10,
|
| 1195 |
+
"outputs": [
|
| 1196 |
+
{
|
| 1197 |
+
"output_type": "stream",
|
| 1198 |
+
"name": "stdout",
|
| 1199 |
+
"text": [
|
| 1200 |
+
"Classes: 101\n"
|
| 1201 |
+
]
|
| 1202 |
+
}
|
| 1203 |
+
]
|
| 1204 |
+
},
|
| 1205 |
+
{
|
| 1206 |
+
"cell_type": "markdown",
|
| 1207 |
+
"source": [
|
| 1208 |
+
"## Load Model\n",
|
| 1209 |
+
"\n",
|
| 1210 |
+
"This model doesn't come with a head, so we need to write the headed model class."
|
| 1211 |
+
],
|
| 1212 |
+
"metadata": {
|
| 1213 |
+
"id": "_69A3AmO81c8"
|
| 1214 |
+
}
|
| 1215 |
+
},
|
| 1216 |
+
{
|
| 1217 |
+
"cell_type": "code",
|
| 1218 |
+
"source": [
|
| 1219 |
+
"import torch.nn as nn\n",
|
| 1220 |
+
"import torch\n",
|
| 1221 |
+
"from transformers import AutoImageProcessor, AutoModel, get_cosine_schedule_with_warmup\n",
|
| 1222 |
+
"\n",
|
| 1223 |
+
"MODEL_NAME = \"facebook/dinov3-vith16plus-pretrain-lvd1689m\"\n",
|
| 1224 |
+
"\n",
|
| 1225 |
+
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
| 1226 |
+
"\n",
|
| 1227 |
+
"\n",
|
| 1228 |
+
"image_processor = AutoImageProcessor.from_pretrained(MODEL_NAME)\n",
|
| 1229 |
+
"backbone = AutoModel.from_pretrained(MODEL_NAME)\n",
|
| 1230 |
+
"\n",
|
| 1231 |
+
"hidden_size = getattr(backbone.config, \"hidden_size\", None)\n",
|
| 1232 |
+
"\n",
|
| 1233 |
+
"class DinoV3Linear(nn.Module):\n",
|
| 1234 |
+
" def __init__(self, backbone: AutoModel, hidden_size: int, num_classes: int, freeze_backbone: bool = True):\n",
|
| 1235 |
+
" super().__init__()\n",
|
| 1236 |
+
" self.backbone = backbone\n",
|
| 1237 |
+
" if freeze_backbone:\n",
|
| 1238 |
+
" for p in self.backbone.parameters():\n",
|
| 1239 |
+
" p.requires_grad = False\n",
|
| 1240 |
+
" self.backbone.eval()\n",
|
| 1241 |
+
"\n",
|
| 1242 |
+
" self.head = nn.Linear(hidden_size, num_classes)\n",
|
| 1243 |
+
"\n",
|
| 1244 |
+
" def forward(self, pixel_values):\n",
|
| 1245 |
+
" outputs = self.backbone(pixel_values=pixel_values)\n",
|
| 1246 |
+
" last_hidden = outputs.last_hidden_state\n",
|
| 1247 |
+
" cls = last_hidden[:, 0]\n",
|
| 1248 |
+
" logits = self.head(cls)\n",
|
| 1249 |
+
" return logits\n",
|
| 1250 |
+
"\n",
|
| 1251 |
+
"model = DinoV3Linear(backbone, hidden_size, num_classes, freeze_backbone=True).to(device) # we only train the head"
|
| 1252 |
+
],
|
| 1253 |
+
"metadata": {
|
| 1254 |
+
"colab": {
|
| 1255 |
+
"base_uri": "https://localhost:8080/",
|
| 1256 |
+
"height": 113,
|
| 1257 |
+
"referenced_widgets": [
|
| 1258 |
+
"32138245d41348928cc5b5834b07cb7e",
|
| 1259 |
+
"df6de04fdb204d348767dd0b2d0e88f7",
|
| 1260 |
+
"63a3800d62dd41d6b4a3f643a8930d95",
|
| 1261 |
+
"49d67bd205184874a5cee04d318d91fe",
|
| 1262 |
+
"f00ace964f96471b9eb839cce48ce378",
|
| 1263 |
+
"3ad0ac8def244930a3aff41d68a88a65",
|
| 1264 |
+
"7464841c193d492685bb929b1c0d230c",
|
| 1265 |
+
"5c16553a2ff34a37a2cb62b4a4c42a6f",
|
| 1266 |
+
"34be83ddb4bf43e58cadbcbac5a606b7",
|
| 1267 |
+
"0ce7bd7e52074f29b446ef2d4dd0921a",
|
| 1268 |
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"7e2178d696c04d5787e736ace9ab57c0",
|
| 1269 |
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"3ff80bc2f64948408757caa8715d0603",
|
| 1270 |
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"12aa8675bca54f05a6deb7ec7a5def7a",
|
| 1271 |
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"31a74feac76f4744a0f34fbc99433831",
|
| 1272 |
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"bd51d97e739a4e78ad28083043f638d8",
|
| 1273 |
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"062d36b5d0c043a597eb9b3ebd35f313",
|
| 1274 |
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"2c2223a6ae3e4ff6be96a5f4e2d2d9b6",
|
| 1275 |
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"f2c7be27f90b49a3abe51b5e3003c17d",
|
| 1276 |
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"76d1f15c857640c3b06d98aef478f234",
|
| 1277 |
+
"d43089f8240c44339c6881355ff0aee3",
|
| 1278 |
+
"a139b85557a942b9b5d32b9d7def3e50",
|
| 1279 |
+
"92043bfce97e4629bf9e4b268aa88c11",
|
| 1280 |
+
"f20b3989658642528f4ed91666320097",
|
| 1281 |
+
"3ee9921a635d44ec9b248e2155b5b243",
|
| 1282 |
+
"caf0790dbf2544378cb04aa8eb3098c3",
|
| 1283 |
+
"3ff0fc5ce62a44b9950dd8575d90bd21",
|
| 1284 |
+
"77cdafc6dae44107a43a46ae19ed390a",
|
| 1285 |
+
"65d8b73e3bdd46fca8a42b67739e27f9",
|
| 1286 |
+
"b566321171044b0eb02ea3bd8c0472df",
|
| 1287 |
+
"62535e046f794a28b4002c3f34fe7ff7",
|
| 1288 |
+
"663aa65fdb4e4349b2815b6bafce4dcd",
|
| 1289 |
+
"8410c9d15bca4c9f8b3aab2b7d327211",
|
| 1290 |
+
"fb359d0651a74fe790aaace9a5d0e329"
|
| 1291 |
+
]
|
| 1292 |
+
},
|
| 1293 |
+
"id": "_oqXAu_y81H4",
|
| 1294 |
+
"outputId": "7c4a4f6f-2301-4a43-eecb-50f1adb004b9"
|
| 1295 |
+
},
|
| 1296 |
+
"execution_count": 11,
|
| 1297 |
+
"outputs": [
|
| 1298 |
+
{
|
| 1299 |
+
"output_type": "display_data",
|
| 1300 |
+
"data": {
|
| 1301 |
+
"text/plain": [
|
| 1302 |
+
"preprocessor_config.json: 0%| | 0.00/585 [00:00<?, ?B/s]"
|
| 1303 |
+
],
|
| 1304 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1305 |
+
"version_major": 2,
|
| 1306 |
+
"version_minor": 0,
|
| 1307 |
+
"model_id": "32138245d41348928cc5b5834b07cb7e"
|
| 1308 |
+
}
|
| 1309 |
+
},
|
| 1310 |
+
"metadata": {}
|
| 1311 |
+
},
|
| 1312 |
+
{
|
| 1313 |
+
"output_type": "display_data",
|
| 1314 |
+
"data": {
|
| 1315 |
+
"text/plain": [
|
| 1316 |
+
"config.json: 0%| | 0.00/744 [00:00<?, ?B/s]"
|
| 1317 |
+
],
|
| 1318 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1319 |
+
"version_major": 2,
|
| 1320 |
+
"version_minor": 0,
|
| 1321 |
+
"model_id": "3ff80bc2f64948408757caa8715d0603"
|
| 1322 |
+
}
|
| 1323 |
+
},
|
| 1324 |
+
"metadata": {}
|
| 1325 |
+
},
|
| 1326 |
+
{
|
| 1327 |
+
"output_type": "display_data",
|
| 1328 |
+
"data": {
|
| 1329 |
+
"text/plain": [
|
| 1330 |
+
"model.safetensors: 0%| | 0.00/3.36G [00:00<?, ?B/s]"
|
| 1331 |
+
],
|
| 1332 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1333 |
+
"version_major": 2,
|
| 1334 |
+
"version_minor": 0,
|
| 1335 |
+
"model_id": "f20b3989658642528f4ed91666320097"
|
| 1336 |
+
}
|
| 1337 |
+
},
|
| 1338 |
+
"metadata": {}
|
| 1339 |
+
}
|
| 1340 |
+
]
|
| 1341 |
+
},
|
| 1342 |
+
{
|
| 1343 |
+
"cell_type": "markdown",
|
| 1344 |
+
"source": [
|
| 1345 |
+
"Write the data collator to batch inputs and dataloaders for training."
|
| 1346 |
+
],
|
| 1347 |
+
"metadata": {
|
| 1348 |
+
"id": "IfC3TFbw9SlZ"
|
| 1349 |
+
}
|
| 1350 |
+
},
|
| 1351 |
+
{
|
| 1352 |
+
"cell_type": "code",
|
| 1353 |
+
"source": [
|
| 1354 |
+
"from dataclasses import dataclass\n",
|
| 1355 |
+
"from PIL import Image\n",
|
| 1356 |
+
"import numpy as np\n",
|
| 1357 |
+
"import torch\n",
|
| 1358 |
+
"from transformers import AutoImageProcessor\n",
|
| 1359 |
+
"\n",
|
| 1360 |
+
"@dataclass\n",
|
| 1361 |
+
"class Collator:\n",
|
| 1362 |
+
" processor: AutoImageProcessor\n",
|
| 1363 |
+
"\n",
|
| 1364 |
+
" def __call__(self, batch):\n",
|
| 1365 |
+
" raw_images = [x[\"image\"] for x in batch]\n",
|
| 1366 |
+
" labels = torch.tensor([x[\"label\"] for x in batch], dtype=torch.long)\n",
|
| 1367 |
+
"\n",
|
| 1368 |
+
" rgb_images = []\n",
|
| 1369 |
+
" # there's grayscale images in the dataset\n",
|
| 1370 |
+
" for im in raw_images:\n",
|
| 1371 |
+
" if isinstance(im, Image.Image):\n",
|
| 1372 |
+
" rgb_images.append(im.convert(\"RGB\"))\n",
|
| 1373 |
+
" continue\n",
|
| 1374 |
+
"\n",
|
| 1375 |
+
" inputs = self.processor(images=rgb_images, return_tensors=\"pt\")\n",
|
| 1376 |
+
" return {\"pixel_values\": inputs[\"pixel_values\"], \"labels\": labels}\n",
|
| 1377 |
+
"\n",
|
| 1378 |
+
"collate_fn = Collator(image_processor)"
|
| 1379 |
+
],
|
| 1380 |
+
"metadata": {
|
| 1381 |
+
"id": "Wlo3_8qE9SVR"
|
| 1382 |
+
},
|
| 1383 |
+
"execution_count": 12,
|
| 1384 |
+
"outputs": []
|
| 1385 |
+
},
|
| 1386 |
+
{
|
| 1387 |
+
"cell_type": "code",
|
| 1388 |
+
"source": [
|
| 1389 |
+
"from torch.utils.data import DataLoader\n",
|
| 1390 |
+
"import os\n",
|
| 1391 |
+
"\n",
|
| 1392 |
+
"BATCH_SIZE = 8\n",
|
| 1393 |
+
"NUM_WORKERS = min(8, os.cpu_count() or 2)\n",
|
| 1394 |
+
"\n",
|
| 1395 |
+
"train_loader = DataLoader(\n",
|
| 1396 |
+
" train_ds,\n",
|
| 1397 |
+
" batch_size=BATCH_SIZE,\n",
|
| 1398 |
+
" shuffle=True,\n",
|
| 1399 |
+
" num_workers=NUM_WORKERS,\n",
|
| 1400 |
+
" pin_memory=True,\n",
|
| 1401 |
+
" collate_fn=collate_fn,\n",
|
| 1402 |
+
")\n",
|
| 1403 |
+
"val_loader = DataLoader(\n",
|
| 1404 |
+
" val_ds,\n",
|
| 1405 |
+
" batch_size=BATCH_SIZE,\n",
|
| 1406 |
+
" shuffle=False,\n",
|
| 1407 |
+
" num_workers=NUM_WORKERS,\n",
|
| 1408 |
+
" pin_memory=True,\n",
|
| 1409 |
+
" collate_fn=collate_fn,\n",
|
| 1410 |
+
")"
|
| 1411 |
+
],
|
| 1412 |
+
"metadata": {
|
| 1413 |
+
"id": "Nou-Ct_e9zV5"
|
| 1414 |
+
},
|
| 1415 |
+
"execution_count": 13,
|
| 1416 |
+
"outputs": []
|
| 1417 |
+
},
|
| 1418 |
+
{
|
| 1419 |
+
"cell_type": "markdown",
|
| 1420 |
+
"source": [
|
| 1421 |
+
"## Training"
|
| 1422 |
+
],
|
| 1423 |
+
"metadata": {
|
| 1424 |
+
"id": "RblgS11W-Wuo"
|
| 1425 |
+
}
|
| 1426 |
+
},
|
| 1427 |
+
{
|
| 1428 |
+
"cell_type": "markdown",
|
| 1429 |
+
"source": [
|
| 1430 |
+
"Find config below."
|
| 1431 |
+
],
|
| 1432 |
+
"metadata": {
|
| 1433 |
+
"id": "25sCxjwG_tPo"
|
| 1434 |
+
}
|
| 1435 |
+
},
|
| 1436 |
+
{
|
| 1437 |
+
"cell_type": "code",
|
| 1438 |
+
"source": [
|
| 1439 |
+
"import math\n",
|
| 1440 |
+
"import random\n",
|
| 1441 |
+
"from typing import List, Dict, Any\n",
|
| 1442 |
+
"\n",
|
| 1443 |
+
"\n",
|
| 1444 |
+
"EPOCHS = 5\n",
|
| 1445 |
+
"LR = 5e-4\n",
|
| 1446 |
+
"WEIGHT_DECAY = 1e-4\n",
|
| 1447 |
+
"WARMUP_RATIO = 0.05\n",
|
| 1448 |
+
"CHECKPOINT_DIR = \"./checkpoints_dinov3_food101\"\n",
|
| 1449 |
+
"EVAL_EVERY_STEPS = 100\n",
|
| 1450 |
+
"\n",
|
| 1451 |
+
"optimizer = torch.optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=LR, weight_decay=WEIGHT_DECAY)\n",
|
| 1452 |
+
"total_steps = EPOCHS * math.ceil(len(train_loader))\n",
|
| 1453 |
+
"warmup_steps = int(WARMUP_RATIO * total_steps)\n",
|
| 1454 |
+
"scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)\n",
|
| 1455 |
+
"criterion = nn.CrossEntropyLoss()\n",
|
| 1456 |
+
"\n",
|
| 1457 |
+
"scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available())"
|
| 1458 |
+
],
|
| 1459 |
+
"metadata": {
|
| 1460 |
+
"colab": {
|
| 1461 |
+
"base_uri": "https://localhost:8080/"
|
| 1462 |
+
},
|
| 1463 |
+
"id": "WWM8KLQD_sya",
|
| 1464 |
+
"outputId": "1672c194-aad2-4af2-a9cf-e61aa0d558b9"
|
| 1465 |
+
},
|
| 1466 |
+
"execution_count": 14,
|
| 1467 |
+
"outputs": [
|
| 1468 |
+
{
|
| 1469 |
+
"output_type": "stream",
|
| 1470 |
+
"name": "stderr",
|
| 1471 |
+
"text": [
|
| 1472 |
+
"/tmp/ipython-input-593493728.py:19: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
|
| 1473 |
+
" scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available())\n"
|
| 1474 |
+
]
|
| 1475 |
+
}
|
| 1476 |
+
]
|
| 1477 |
+
},
|
| 1478 |
+
{
|
| 1479 |
+
"cell_type": "code",
|
| 1480 |
+
"source": [
|
| 1481 |
+
"os.makedirs(\"./checkpoints_dinov3_food101\")"
|
| 1482 |
+
],
|
| 1483 |
+
"metadata": {
|
| 1484 |
+
"id": "OJPRRz09kxFT"
|
| 1485 |
+
},
|
| 1486 |
+
"execution_count": 15,
|
| 1487 |
+
"outputs": []
|
| 1488 |
+
},
|
| 1489 |
+
{
|
| 1490 |
+
"cell_type": "markdown",
|
| 1491 |
+
"source": [
|
| 1492 |
+
"We need to evaluate during training."
|
| 1493 |
+
],
|
| 1494 |
+
"metadata": {
|
| 1495 |
+
"id": "FHS5DSu1_22g"
|
| 1496 |
+
}
|
| 1497 |
+
},
|
| 1498 |
+
{
|
| 1499 |
+
"cell_type": "code",
|
| 1500 |
+
"source": [
|
| 1501 |
+
"def evaluate() -> Dict[str, float]:\n",
|
| 1502 |
+
" model.eval()\n",
|
| 1503 |
+
" correct, total, loss_sum = 0, 0, 0.0\n",
|
| 1504 |
+
" with torch.no_grad():\n",
|
| 1505 |
+
" for batch in val_loader:\n",
|
| 1506 |
+
" pixel_values = batch[\"pixel_values\"].to(device, non_blocking=True)\n",
|
| 1507 |
+
" labels = batch[\"labels\"].to(device, non_blocking=True)\n",
|
| 1508 |
+
" logits = model(pixel_values)\n",
|
| 1509 |
+
" loss = criterion(logits, labels)\n",
|
| 1510 |
+
" loss_sum += loss.item() * labels.size(0)\n",
|
| 1511 |
+
" preds = logits.argmax(dim=-1)\n",
|
| 1512 |
+
" correct += (preds == labels).sum().item()\n",
|
| 1513 |
+
" total += labels.size(0)\n",
|
| 1514 |
+
" return {\n",
|
| 1515 |
+
" \"val_loss\": loss_sum / max(total, 1),\n",
|
| 1516 |
+
" \"val_acc\": correct / max(total, 1),\n",
|
| 1517 |
+
" }"
|
| 1518 |
+
],
|
| 1519 |
+
"metadata": {
|
| 1520 |
+
"id": "TSD4tzZr_4i3"
|
| 1521 |
+
},
|
| 1522 |
+
"execution_count": 16,
|
| 1523 |
+
"outputs": []
|
| 1524 |
+
},
|
| 1525 |
+
{
|
| 1526 |
+
"cell_type": "markdown",
|
| 1527 |
+
"source": [
|
| 1528 |
+
"Let's write the training loop. We'll also use trackio for experiment tracking."
|
| 1529 |
+
],
|
| 1530 |
+
"metadata": {
|
| 1531 |
+
"id": "yakvOUOkAVcR"
|
| 1532 |
+
}
|
| 1533 |
+
},
|
| 1534 |
+
{
|
| 1535 |
+
"cell_type": "code",
|
| 1536 |
+
"execution_count": 17,
|
| 1537 |
+
"metadata": {
|
| 1538 |
+
"colab": {
|
| 1539 |
+
"base_uri": "https://localhost:8080/",
|
| 1540 |
+
"height": 723
|
| 1541 |
+
},
|
| 1542 |
+
"id": "r-WQGd7UyN1s",
|
| 1543 |
+
"outputId": "83e5aa69-4e4d-4c1d-c045-fc8ebad975ff"
|
| 1544 |
+
},
|
| 1545 |
+
"outputs": [
|
| 1546 |
+
{
|
| 1547 |
+
"output_type": "stream",
|
| 1548 |
+
"name": "stdout",
|
| 1549 |
+
"text": [
|
| 1550 |
+
"* Running on public URL: https://3669a91d39321f7f86.gradio.live\n",
|
| 1551 |
+
"* Trackio project initialized: dinov3\n",
|
| 1552 |
+
"* Trackio metrics logged to: /root/.cache/huggingface/trackio\n",
|
| 1553 |
+
"* View dashboard by running in your terminal:\n",
|
| 1554 |
+
"\u001b[1m\u001b[93mtrackio show --project \"dinov3\"\u001b[0m\n",
|
| 1555 |
+
"* or by running in Python: trackio.show(project=\"dinov3\")\n",
|
| 1556 |
+
"[epoch 1 | step 100] train_loss=4.4878 val_loss=4.0990 val_acc=50.77%\n",
|
| 1557 |
+
"[epoch 1 | step 200] train_loss=3.4722 val_loss=2.5605 val_acc=83.72%\n",
|
| 1558 |
+
"[epoch 1 | step 300] train_loss=1.9046 val_loss=1.2049 val_acc=87.09%\n",
|
| 1559 |
+
"[epoch 1 | step 400] train_loss=1.0664 val_loss=0.7385 val_acc=89.78%\n",
|
| 1560 |
+
"[epoch 1 | step 500] train_loss=0.7269 val_loss=0.5500 val_acc=90.30%\n"
|
| 1561 |
+
]
|
| 1562 |
+
},
|
| 1563 |
+
{
|
| 1564 |
+
"output_type": "stream",
|
| 1565 |
+
"name": "stderr",
|
| 1566 |
+
"text": [
|
| 1567 |
+
"/usr/local/lib/python3.11/dist-packages/PIL/TiffImagePlugin.py:950: UserWarning: Truncated File Read\n",
|
| 1568 |
+
" warnings.warn(str(msg))\n"
|
| 1569 |
+
]
|
| 1570 |
+
},
|
| 1571 |
+
{
|
| 1572 |
+
"output_type": "stream",
|
| 1573 |
+
"name": "stdout",
|
| 1574 |
+
"text": [
|
| 1575 |
+
"[epoch 1 | step 600] train_loss=0.6400 val_loss=0.4473 val_acc=91.92%\n",
|
| 1576 |
+
"[epoch 1 | step 700] train_loss=0.5444 val_loss=0.3916 val_acc=92.44%\n",
|
| 1577 |
+
"[epoch 1 | step 800] train_loss=0.5084 val_loss=0.3506 val_acc=92.08%\n"
|
| 1578 |
+
]
|
| 1579 |
+
},
|
| 1580 |
+
{
|
| 1581 |
+
"output_type": "error",
|
| 1582 |
+
"ename": "KeyboardInterrupt",
|
| 1583 |
+
"evalue": "",
|
| 1584 |
+
"traceback": [
|
| 1585 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 1586 |
+
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
| 1587 |
+
"\u001b[0;32m/tmp/ipython-input-3838929309.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1588 |
+
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/amp/grad_scaler.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, optimizer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 455\u001b[0m ), \"No inf checks were recorded for this optimizer.\"\n\u001b[1;32m 456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m \u001b[0mretval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_opt_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0moptimizer_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stage\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOptState\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSTEPPED\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1589 |
+
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/amp/grad_scaler.py\u001b[0m in \u001b[0;36m_maybe_opt_step\u001b[0;34m(self, optimizer, optimizer_state, *args, **kwargs)\u001b[0m\n\u001b[1;32m 349\u001b[0m ) -> Optional[float]:\n\u001b[1;32m 350\u001b[0m \u001b[0mretval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizer_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"found_inf_per_device\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 352\u001b[0m \u001b[0mretval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1590 |
+
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/amp/grad_scaler.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 349\u001b[0m ) -> Optional[float]:\n\u001b[1;32m 350\u001b[0m \u001b[0mretval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizer_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"found_inf_per_device\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 352\u001b[0m \u001b[0mretval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1591 |
+
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
| 1592 |
+
]
|
| 1593 |
+
}
|
| 1594 |
+
],
|
| 1595 |
+
"source": [
|
| 1596 |
+
"import trackio\n",
|
| 1597 |
+
"\n",
|
| 1598 |
+
"best_acc = 0.0\n",
|
| 1599 |
+
"global_step = 0\n",
|
| 1600 |
+
"\n",
|
| 1601 |
+
"trackio.init(project=\"dinov3\", config={\n",
|
| 1602 |
+
" \"epochs\": EPOCHS,\n",
|
| 1603 |
+
" \"learning_rate\": LR,\n",
|
| 1604 |
+
" \"batch_size\": BATCH_SIZE\n",
|
| 1605 |
+
" })\n",
|
| 1606 |
+
"\n",
|
| 1607 |
+
"for epoch in range(1, EPOCHS + 1):\n",
|
| 1608 |
+
" model.train()\n",
|
| 1609 |
+
" model.backbone.eval() # comment out if you want to train the whole model\n",
|
| 1610 |
+
"\n",
|
| 1611 |
+
" running_loss = 0.0\n",
|
| 1612 |
+
" for i, batch in enumerate(train_loader, start=1):\n",
|
| 1613 |
+
" pixel_values = batch[\"pixel_values\"].to(device, non_blocking=True)\n",
|
| 1614 |
+
" labels = batch[\"labels\"].to(device, non_blocking=True)\n",
|
| 1615 |
+
"\n",
|
| 1616 |
+
" optimizer.zero_grad(set_to_none=True)\n",
|
| 1617 |
+
" logits = model(pixel_values)\n",
|
| 1618 |
+
" loss = criterion(logits, labels)\n",
|
| 1619 |
+
"\n",
|
| 1620 |
+
" scaler.scale(loss).backward()\n",
|
| 1621 |
+
" scaler.step(optimizer)\n",
|
| 1622 |
+
" scaler.update()\n",
|
| 1623 |
+
" scheduler.step()\n",
|
| 1624 |
+
"\n",
|
| 1625 |
+
" running_loss += loss.item()\n",
|
| 1626 |
+
" global_step += 1\n",
|
| 1627 |
+
"\n",
|
| 1628 |
+
" if global_step % EVAL_EVERY_STEPS == 0:\n",
|
| 1629 |
+
" metrics = evaluate()\n",
|
| 1630 |
+
" print(\n",
|
| 1631 |
+
" f\"[epoch {epoch} | step {global_step}] \"\n",
|
| 1632 |
+
" f\"train_loss={running_loss / EVAL_EVERY_STEPS:.4f} \"\n",
|
| 1633 |
+
" f\"val_loss={metrics['val_loss']:.4f} val_acc={metrics['val_acc']*100:.2f}%\"\n",
|
| 1634 |
+
" )\n",
|
| 1635 |
+
" running_loss = 0.0\n",
|
| 1636 |
+
"\n",
|
| 1637 |
+
" trackio.log(\n",
|
| 1638 |
+
" {\n",
|
| 1639 |
+
" \"epoch\": epoch,\n",
|
| 1640 |
+
" \"val_acc\": best_acc,\n",
|
| 1641 |
+
" }\n",
|
| 1642 |
+
" )\n",
|
| 1643 |
+
"\n",
|
| 1644 |
+
" if metrics[\"val_acc\"] > best_acc:\n",
|
| 1645 |
+
" best_acc = metrics[\"val_acc\"]\n",
|
| 1646 |
+
" ckpt_path = os.path.join(CHECKPOINT_DIR, f\"best_acc_{best_acc:.4f}.pt\")\n",
|
| 1647 |
+
" torch.save(\n",
|
| 1648 |
+
" {\n",
|
| 1649 |
+
" \"model_state_dict\": model.state_dict(),\n",
|
| 1650 |
+
" \"optimizer_state_dict\": optimizer.state_dict(),\n",
|
| 1651 |
+
" \"scheduler_state_dict\": scheduler.state_dict(),\n",
|
| 1652 |
+
" \"config\": {\n",
|
| 1653 |
+
" \"model_name\": MODEL_NAME,\n",
|
| 1654 |
+
" \"num_classes\": num_classes,\n",
|
| 1655 |
+
" },\n",
|
| 1656 |
+
" \"step\": global_step,\n",
|
| 1657 |
+
" \"epoch\": epoch,\n",
|
| 1658 |
+
" },\n",
|
| 1659 |
+
" ckpt_path,\n",
|
| 1660 |
+
" )\n",
|
| 1661 |
+
"\n",
|
| 1662 |
+
"\n",
|
| 1663 |
+
" metrics = evaluate()\n",
|
| 1664 |
+
" print(\n",
|
| 1665 |
+
" f\"END EPOCH {epoch}: val_loss={metrics['val_loss']:.4f} val_acc={metrics['val_acc']*100:.2f}% \"\n",
|
| 1666 |
+
" f\"(best_acc={best_acc*100:.2f}%)\"\n",
|
| 1667 |
+
" )\n",
|
| 1668 |
+
" trackio.finish()"
|
| 1669 |
+
]
|
| 1670 |
+
},
|
| 1671 |
+
{
|
| 1672 |
+
"cell_type": "code",
|
| 1673 |
+
"source": [
|
| 1674 |
+
"!trackio show"
|
| 1675 |
+
],
|
| 1676 |
+
"metadata": {
|
| 1677 |
+
"id": "dX0kEHogATQ_"
|
| 1678 |
+
},
|
| 1679 |
+
"execution_count": null,
|
| 1680 |
+
"outputs": []
|
| 1681 |
+
},
|
| 1682 |
+
{
|
| 1683 |
+
"cell_type": "markdown",
|
| 1684 |
+
"source": [
|
| 1685 |
+
"Let's infer with the model, I have a few in the wild images."
|
| 1686 |
+
],
|
| 1687 |
+
"metadata": {
|
| 1688 |
+
"id": "VKpGJ4L7bb2E"
|
| 1689 |
+
}
|
| 1690 |
+
},
|
| 1691 |
+
{
|
| 1692 |
+
"cell_type": "code",
|
| 1693 |
+
"source": [
|
| 1694 |
+
"import torch\n",
|
| 1695 |
+
"from PIL import Image\n",
|
| 1696 |
+
"from typing import List, Dict\n",
|
| 1697 |
+
"\n",
|
| 1698 |
+
"\n",
|
| 1699 |
+
"model.eval()\n",
|
| 1700 |
+
"\n",
|
| 1701 |
+
"images = [\"/content/pizza.jpg\", \"/content/spaghetti.JPG\"]\n",
|
| 1702 |
+
"\n",
|
| 1703 |
+
"pil_images = [Image.open(p).convert(\"RGB\") for p in images]\n",
|
| 1704 |
+
"inputs = image_processor(images=pil_images, return_tensors=\"pt\").to(device)\n",
|
| 1705 |
+
"\n",
|
| 1706 |
+
"with torch.no_grad():\n",
|
| 1707 |
+
" logits = model(inputs[\"pixel_values\"])\n",
|
| 1708 |
+
"\n",
|
| 1709 |
+
"# take top 2 classes\n",
|
| 1710 |
+
"probs = logits.softmax(dim=-1)\n",
|
| 1711 |
+
"scores, indices = probs.topk(2, dim=-1)\n",
|
| 1712 |
+
"\n",
|
| 1713 |
+
"results = []\n",
|
| 1714 |
+
"for path, idxs, scs in zip(images, indices, scores):\n",
|
| 1715 |
+
" preds = [\n",
|
| 1716 |
+
" {\"label_id\": int(i.item()),\n",
|
| 1717 |
+
" \"label\": id2label.get(int(i.item()), f\"class_{int(i)}\"),\n",
|
| 1718 |
+
" \"score\": float(s.item())}\n",
|
| 1719 |
+
" for i, s in zip(idxs, scs)\n",
|
| 1720 |
+
" ]\n",
|
| 1721 |
+
" results.append({\"image\": path, \"topk\": preds})\n"
|
| 1722 |
+
],
|
| 1723 |
+
"metadata": {
|
| 1724 |
+
"id": "RGZntYQEaVbA"
|
| 1725 |
+
},
|
| 1726 |
+
"execution_count": 19,
|
| 1727 |
+
"outputs": []
|
| 1728 |
+
},
|
| 1729 |
+
{
|
| 1730 |
+
"cell_type": "markdown",
|
| 1731 |
+
"source": [
|
| 1732 |
+
"The model predicts correctly, which is expected given we only trained head with the great backbone frozen, it learned very fast. Feel free to try with more challenging use cases."
|
| 1733 |
+
],
|
| 1734 |
+
"metadata": {
|
| 1735 |
+
"id": "bFoB-1Ebcab1"
|
| 1736 |
+
}
|
| 1737 |
+
},
|
| 1738 |
+
{
|
| 1739 |
+
"cell_type": "code",
|
| 1740 |
+
"source": [
|
| 1741 |
+
"results"
|
| 1742 |
+
],
|
| 1743 |
+
"metadata": {
|
| 1744 |
+
"colab": {
|
| 1745 |
+
"base_uri": "https://localhost:8080/"
|
| 1746 |
+
},
|
| 1747 |
+
"id": "NrgtO2D1cXzj",
|
| 1748 |
+
"outputId": "c972e7d0-ee78-45d3-e91f-7c68521d6a0b"
|
| 1749 |
+
},
|
| 1750 |
+
"execution_count": 20,
|
| 1751 |
+
"outputs": [
|
| 1752 |
+
{
|
| 1753 |
+
"output_type": "execute_result",
|
| 1754 |
+
"data": {
|
| 1755 |
+
"text/plain": [
|
| 1756 |
+
"[{'image': '/content/pizza.jpg',\n",
|
| 1757 |
+
" 'topk': [{'label_id': 76, 'label': 'pizza', 'score': 0.7595003843307495},\n",
|
| 1758 |
+
" {'label_id': 35, 'label': 'escargots', 'score': 0.013227012008428574}]},\n",
|
| 1759 |
+
" {'image': '/content/spaghetti.JPG',\n",
|
| 1760 |
+
" 'topk': [{'label_id': 91,\n",
|
| 1761 |
+
" 'label': 'spaghetti_carbonara',\n",
|
| 1762 |
+
" 'score': 0.6622196435928345},\n",
|
| 1763 |
+
" {'label_id': 90,\n",
|
| 1764 |
+
" 'label': 'spaghetti_bolognese',\n",
|
| 1765 |
+
" 'score': 0.18182380497455597}]}]"
|
| 1766 |
+
]
|
| 1767 |
+
},
|
| 1768 |
+
"metadata": {},
|
| 1769 |
+
"execution_count": 20
|
| 1770 |
+
}
|
| 1771 |
+
]
|
| 1772 |
+
}
|
| 1773 |
+
]
|
| 1774 |
+
}
|