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Ensemble_Version_2

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+ "model_module": "@jupyter-widgets/controls",
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+ "model_name": "DescriptionStyleModel",
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+ "model_module_version": "1.5.0",
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+ "state": {
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+ "_model_module": "@jupyter-widgets/controls",
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+ "_model_module_version": "1.5.0",
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+ "_view_module_version": "1.2.0",
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+ "_view_name": "StyleView",
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+ "description_width": ""
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+ }
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+ }
1049
+ }
1050
+ }
1051
+ },
1052
+ "cells": [
1053
+ {
1054
+ "cell_type": "markdown",
1055
+ "source": [
1056
+ "# Imports and Hugging Face Login"
1057
+ ],
1058
+ "metadata": {
1059
+ "id": "GirPusJtYPsP"
1060
+ }
1061
+ },
1062
+ {
1063
+ "cell_type": "code",
1064
+ "execution_count": 1,
1065
+ "metadata": {
1066
+ "id": "hKdN-6CXXV12",
1067
+ "colab": {
1068
+ "base_uri": "https://localhost:8080/"
1069
+ },
1070
+ "outputId": "a1d9a131-8a76-436c-ac24-c3467b5dcc01"
1071
+ },
1072
+ "outputs": [
1073
+ {
1074
+ "output_type": "stream",
1075
+ "name": "stdout",
1076
+ "text": [
1077
+ "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (0.26.5)\n",
1078
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (3.16.1)\n",
1079
+ "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2024.10.0)\n",
1080
+ "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (24.2)\n",
1081
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (6.0.2)\n",
1082
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2.32.3)\n",
1083
+ "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.66.6)\n",
1084
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.12.2)\n",
1085
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.4.0)\n",
1086
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.10)\n",
1087
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2.2.3)\n",
1088
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2024.8.30)\n",
1089
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
1090
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
1091
+ "\u001b[0m"
1092
+ ]
1093
+ }
1094
+ ],
1095
+ "source": [
1096
+ "!pip install huggingface-hub\n",
1097
+ "!pip install datasets > delete.txt"
1098
+ ]
1099
+ },
1100
+ {
1101
+ "cell_type": "code",
1102
+ "source": [
1103
+ "import torch\n",
1104
+ "import pickle\n",
1105
+ "from huggingface_hub import hf_hub_download\n",
1106
+ "from datasets import load_dataset, Image\n",
1107
+ "import torch\n",
1108
+ "from torch import nn, optim\n",
1109
+ "from torch.utils.data import DataLoader, Dataset\n",
1110
+ "import numpy as np\n",
1111
+ "from geopy.distance import geodesic\n",
1112
+ "import matplotlib.pyplot as plt\n",
1113
+ "from torchvision import models"
1114
+ ],
1115
+ "metadata": {
1116
+ "id": "SPzgZOzxYYiT"
1117
+ },
1118
+ "execution_count": 2,
1119
+ "outputs": []
1120
+ },
1121
+ {
1122
+ "cell_type": "code",
1123
+ "source": [
1124
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
1125
+ "print(device)"
1126
+ ],
1127
+ "metadata": {
1128
+ "id": "PJquO0g1YaMU",
1129
+ "colab": {
1130
+ "base_uri": "https://localhost:8080/"
1131
+ },
1132
+ "outputId": "d82f4fdc-32ee-4f91-e6ce-558ad3e3c837"
1133
+ },
1134
+ "execution_count": 3,
1135
+ "outputs": [
1136
+ {
1137
+ "output_type": "stream",
1138
+ "name": "stdout",
1139
+ "text": [
1140
+ "cuda\n"
1141
+ ]
1142
+ }
1143
+ ]
1144
+ },
1145
+ {
1146
+ "cell_type": "code",
1147
+ "source": [
1148
+ "!huggingface-cli login\n",
1149
+ "# use appropiate token"
1150
+ ],
1151
+ "metadata": {
1152
+ "id": "IcGfZSsoZgau",
1153
+ "colab": {
1154
+ "base_uri": "https://localhost:8080/"
1155
+ },
1156
+ "outputId": "436dcc6f-a924-4be8-e9a8-39c197e5e1e1"
1157
+ },
1158
+ "execution_count": 4,
1159
+ "outputs": [
1160
+ {
1161
+ "output_type": "stream",
1162
+ "name": "stdout",
1163
+ "text": [
1164
+ "\n",
1165
+ " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
1166
+ " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
1167
+ " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
1168
+ " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
1169
+ " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
1170
+ "\n",
1171
+ " To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
1172
+ "Enter your token (input will not be visible): \n",
1173
+ "Add token as git credential? (Y/n) y\n",
1174
+ "Token is valid (permission: fineGrained).\n",
1175
+ "The token `CIS 5190 Project 3` has been saved to /root/.cache/huggingface/stored_tokens\n",
1176
+ "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
1177
+ "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
1178
+ "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
1179
+ "\n",
1180
+ "git config --global credential.helper store\n",
1181
+ "\n",
1182
+ "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
1183
+ "Token has not been saved to git credential helper.\n",
1184
+ "Your token has been saved to /root/.cache/huggingface/token\n",
1185
+ "Login successful.\n",
1186
+ "The current active token is: `CIS 5190 Project 3`\n"
1187
+ ]
1188
+ }
1189
+ ]
1190
+ },
1191
+ {
1192
+ "cell_type": "markdown",
1193
+ "source": [
1194
+ "# Models and Classes"
1195
+ ],
1196
+ "metadata": {
1197
+ "id": "LplsJ-PXXbtm"
1198
+ }
1199
+ },
1200
+ {
1201
+ "cell_type": "code",
1202
+ "source": [
1203
+ "class EnsembleModel(nn.Module):\n",
1204
+ " def __init__(self, models, num_models):\n",
1205
+ " super(EnsembleModel, self).__init__()\n",
1206
+ " self.models = nn.ModuleList(models)\n",
1207
+ " self.weights = nn.Parameter(torch.ones(num_models) / num_models)\n",
1208
+ "\n",
1209
+ " def forward(self, x):\n",
1210
+ " outputs = torch.stack([model(x) for model in self.models], dim=-1)\n",
1211
+ " weighted_output = torch.einsum('bij,j->bi', outputs, self.weights)\n",
1212
+ " return weighted_output"
1213
+ ],
1214
+ "metadata": {
1215
+ "id": "ofOTpLIPcylC"
1216
+ },
1217
+ "execution_count": 9,
1218
+ "outputs": []
1219
+ },
1220
+ {
1221
+ "cell_type": "code",
1222
+ "source": [
1223
+ "class Model1(nn.Module):\n",
1224
+ " def __init__(self, dropout):\n",
1225
+ " super(Model1, self).__init__()\n",
1226
+ " self.features = nn.Sequential(\n",
1227
+ " nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n",
1228
+ " nn.ReLU(inplace=True),\n",
1229
+ " nn.MaxPool2d(kernel_size=3, stride=2),\n",
1230
+ " nn.Conv2d(64, 192, kernel_size=5, padding=2),\n",
1231
+ " nn.ReLU(inplace=True),\n",
1232
+ " nn.MaxPool2d(kernel_size=3, stride=2),\n",
1233
+ " nn.Conv2d(192, 384, kernel_size=3, padding=1),\n",
1234
+ " nn.ReLU(inplace=True),\n",
1235
+ " nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
1236
+ " nn.ReLU(inplace=True),\n",
1237
+ " nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
1238
+ " nn.ReLU(inplace=True),\n",
1239
+ " nn.MaxPool2d(kernel_size=3, stride=2),\n",
1240
+ " )\n",
1241
+ " self.classifier = nn.Sequential(\n",
1242
+ " nn.Dropout(p=dropout),\n",
1243
+ " nn.Linear(256 * 6 * 6, 1024),\n",
1244
+ " nn.ReLU(inplace=True),\n",
1245
+ " nn.Dropout(p=dropout),\n",
1246
+ " nn.Linear(1024, 512),\n",
1247
+ " nn.ReLU(inplace=True),\n",
1248
+ " nn.Linear(512, 2),\n",
1249
+ " )\n",
1250
+ "\n",
1251
+ " def forward(self, x):\n",
1252
+ " x = self.features(x)\n",
1253
+ " x = torch.flatten(x, 1)\n",
1254
+ " x = self.classifier(x)\n",
1255
+ " return x\n",
1256
+ "\n",
1257
+ "\n",
1258
+ "def model_fn(dropout):\n",
1259
+ " return Model1(dropout)"
1260
+ ],
1261
+ "metadata": {
1262
+ "id": "fbtZvQrlYGfU"
1263
+ },
1264
+ "execution_count": 10,
1265
+ "outputs": []
1266
+ },
1267
+ {
1268
+ "cell_type": "code",
1269
+ "source": [
1270
+ "class Model2(nn.Module):\n",
1271
+ " def __init__(self, num_blocks=3, dropout_rate=0.5):\n",
1272
+ " super(Model2, self).__init__()\n",
1273
+ "\n",
1274
+ " resnet = models.resnet34(pretrained=True)\n",
1275
+ "\n",
1276
+ " for param in list(resnet.parameters())[:num_blocks]:\n",
1277
+ " param.requires_grad = False\n",
1278
+ "\n",
1279
+ " self.features = nn.Sequential(*list(resnet.children())[:-2])\n",
1280
+ " self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
1281
+ "\n",
1282
+ " self.classifier = nn.Sequential(\n",
1283
+ " nn.Flatten(),\n",
1284
+ " nn.Dropout(p=dropout_rate),\n",
1285
+ " nn.Linear(resnet.fc.in_features, 512),\n",
1286
+ " nn.ReLU(inplace=True),\n",
1287
+ " nn.Dropout(p=dropout_rate),\n",
1288
+ " nn.Linear(512, 2)\n",
1289
+ " )\n",
1290
+ "\n",
1291
+ " def forward(self, x):\n",
1292
+ " x = self.features(x)\n",
1293
+ " x = self.avgpool(x)\n",
1294
+ " x = self.classifier(x)\n",
1295
+ " return x"
1296
+ ],
1297
+ "metadata": {
1298
+ "id": "iBssHEtGXdWi"
1299
+ },
1300
+ "execution_count": 11,
1301
+ "outputs": []
1302
+ },
1303
+ {
1304
+ "cell_type": "code",
1305
+ "source": [
1306
+ "class InceptionModule(nn.Module):\n",
1307
+ " def __init__(self, in_channels, ch1x1, ch3x3_reduce, ch3x3, ch5x5_reduce, ch5x5, pool_proj):\n",
1308
+ " super(InceptionModule, self).__init__()\n",
1309
+ "\n",
1310
+ " self.branch1 = nn.Sequential(\n",
1311
+ " nn.Conv2d(in_channels, ch1x1, kernel_size=1),\n",
1312
+ " nn.ReLU(inplace=True)\n",
1313
+ " )\n",
1314
+ " self.branch2 = nn.Sequential(\n",
1315
+ " nn.Conv2d(in_channels, ch3x3_reduce, kernel_size=1),\n",
1316
+ " nn.ReLU(inplace=True),\n",
1317
+ " nn.Conv2d(ch3x3_reduce, ch3x3, kernel_size=3, padding=1),\n",
1318
+ " nn.ReLU(inplace=True)\n",
1319
+ " )\n",
1320
+ "\n",
1321
+ " self.branch3 = nn.Sequential(\n",
1322
+ " nn.Conv2d(in_channels, ch5x5_reduce, kernel_size=1),\n",
1323
+ " nn.ReLU(inplace=True),\n",
1324
+ " nn.Conv2d(ch5x5_reduce, ch5x5, kernel_size=5, padding=2),\n",
1325
+ " nn.ReLU(inplace=True)\n",
1326
+ " )\n",
1327
+ "\n",
1328
+ " self.branch4 = nn.Sequential(\n",
1329
+ " nn.MaxPool2d(kernel_size=3, stride=1, padding=1),\n",
1330
+ " nn.Conv2d(in_channels, pool_proj, kernel_size=1),\n",
1331
+ " nn.ReLU(inplace=True)\n",
1332
+ " )\n",
1333
+ "\n",
1334
+ " def forward(self, x):\n",
1335
+ " branch1 = self.branch1(x)\n",
1336
+ " branch2 = self.branch2(x)\n",
1337
+ " branch3 = self.branch3(x)\n",
1338
+ " branch4 = self.branch4(x)\n",
1339
+ " outputs = torch.cat([branch1, branch2, branch3, branch4], 1)\n",
1340
+ " return outputs\n",
1341
+ "\n",
1342
+ "class Model4(nn.Module):\n",
1343
+ " def __init__(self, dropout_rate=0.5):\n",
1344
+ " super(Model4, self).__init__()\n",
1345
+ "\n",
1346
+ " self.pre_layers = nn.Sequential(\n",
1347
+ " nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),\n",
1348
+ " nn.ReLU(inplace=True),\n",
1349
+ " nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n",
1350
+ " nn.Conv2d(64, 192, kernel_size=3, padding=1),\n",
1351
+ " nn.ReLU(inplace=True),\n",
1352
+ " nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
1353
+ " )\n",
1354
+ "\n",
1355
+ "\n",
1356
+ " self.inception1 = InceptionModule(192, 64, 96, 128, 16, 32, 32)\n",
1357
+ " self.inception2 = InceptionModule(256, 128, 128, 192, 32, 96, 64)\n",
1358
+ "\n",
1359
+ " self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
1360
+ "\n",
1361
+ " self.inception3 = InceptionModule(480, 192, 96, 208, 16, 48, 64)\n",
1362
+ " self.inception4 = InceptionModule(512, 160, 112, 224, 24, 64, 64)\n",
1363
+ "\n",
1364
+ " self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
1365
+ " self.classifier = nn.Sequential(\n",
1366
+ " nn.Flatten(),\n",
1367
+ " nn.Dropout(p=dropout_rate),\n",
1368
+ " nn.Linear(512, 1024),\n",
1369
+ " nn.ReLU(inplace=True),\n",
1370
+ " nn.Dropout(p=dropout_rate),\n",
1371
+ " nn.Linear(1024, 512),\n",
1372
+ " nn.ReLU(inplace=True),\n",
1373
+ " nn.Linear(512, 2)\n",
1374
+ " )\n",
1375
+ "\n",
1376
+ " def forward(self, x):\n",
1377
+ " x = self.pre_layers(x)\n",
1378
+ " x = self.inception1(x)\n",
1379
+ " x = self.inception2(x)\n",
1380
+ " x = self.maxpool(x)\n",
1381
+ " x = self.inception3(x)\n",
1382
+ " x = self.inception4(x)\n",
1383
+ " x = self.avgpool(x)\n",
1384
+ " x = self.classifier(x)\n",
1385
+ " return x"
1386
+ ],
1387
+ "metadata": {
1388
+ "id": "c4y6R0A3XjcI"
1389
+ },
1390
+ "execution_count": 12,
1391
+ "outputs": []
1392
+ },
1393
+ {
1394
+ "cell_type": "markdown",
1395
+ "source": [
1396
+ "# Load Test Dataset"
1397
+ ],
1398
+ "metadata": {
1399
+ "id": "ybwRXm3zYg_I"
1400
+ }
1401
+ },
1402
+ {
1403
+ "cell_type": "code",
1404
+ "source": [
1405
+ "from torch.utils.data import Dataset\n",
1406
+ "class GPSImageDataset(Dataset):\n",
1407
+ " def __init__(self, hf_dataset, transform, lat_mean=None, lat_std=None, lon_mean=None, lon_std=None):\n",
1408
+ " self.hf_dataset = hf_dataset\n",
1409
+ " self.transform = transform\n",
1410
+ "\n",
1411
+ " # Normalize the latitude and longitude\n",
1412
+ " self.latitudes = np.array(hf_dataset['Latitude'])\n",
1413
+ " self.longitudes = np.array(hf_dataset['Longitude'])\n",
1414
+ " self.latitude_mean = lat_mean if lat_mean is not None else self.latitudes.mean()\n",
1415
+ " self.latitude_std = lat_std if lat_std is not None else self.latitudes.std()\n",
1416
+ " self.longitude_mean = lon_mean if lon_mean is not None else self.longitudes.mean()\n",
1417
+ " self.longitude_std = lon_std if lon_std is not None else self.longitudes.std()\n",
1418
+ "\n",
1419
+ " self.normalized_latitudes = (self.latitudes - self.latitude_mean) / self.latitude_std\n",
1420
+ " self.normalized_longitudes = (self.longitudes - self.longitude_mean) / self.longitude_std\n",
1421
+ "\n",
1422
+ " def __len__(self):\n",
1423
+ " return len(self.hf_dataset)\n",
1424
+ "\n",
1425
+ " def __getitem__(self, idx):\n",
1426
+ " image = self.hf_dataset[idx]['image']\n",
1427
+ " latitude = self.normalized_latitudes[idx]\n",
1428
+ " longitude = self.normalized_longitudes[idx]\n",
1429
+ "\n",
1430
+ " if self.transform:\n",
1431
+ " image = self.transform(image)\n",
1432
+ "\n",
1433
+ " return image, torch.tensor([latitude, longitude], dtype=torch.float)"
1434
+ ],
1435
+ "metadata": {
1436
+ "id": "EfCxgZxMY7b6"
1437
+ },
1438
+ "execution_count": 14,
1439
+ "outputs": []
1440
+ },
1441
+ {
1442
+ "cell_type": "code",
1443
+ "source": [
1444
+ "from torchvision import transforms, models\n",
1445
+ "transform = transforms.Compose([\n",
1446
+ " transforms.RandomResizedCrop(224),\n",
1447
+ " transforms.RandomHorizontalFlip(),\n",
1448
+ " transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n",
1449
+ " transforms.ToTensor(),\n",
1450
+ " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
1451
+ "])\n",
1452
+ "\n",
1453
+ "inference_transform = transforms.Compose([\n",
1454
+ " transforms.Resize((224, 224)),\n",
1455
+ " transforms.ToTensor(),\n",
1456
+ " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
1457
+ "])"
1458
+ ],
1459
+ "metadata": {
1460
+ "id": "P4Gx6KLQXz4E"
1461
+ },
1462
+ "execution_count": 15,
1463
+ "outputs": []
1464
+ },
1465
+ {
1466
+ "cell_type": "code",
1467
+ "source": [
1468
+ "dataset_test = load_dataset(\"gydou/released_img\")"
1469
+ ],
1470
+ "metadata": {
1471
+ "id": "NTFvFWpRYgcM",
1472
+ "colab": {
1473
+ "base_uri": "https://localhost:8080/",
1474
+ "height": 217,
1475
+ "referenced_widgets": [
1476
+ "c46dc091acd34be2887c59bf95838529",
1477
+ "af6cce709f8a478a87c4c89222193d8b",
1478
+ "4c32916fe36e4466b9c8a96bbd7db71b",
1479
+ "48d80beb4cc646328036336431b01278",
1480
+ "755b9a30f525493382f771840e4b04f4",
1481
+ "481a5ffb8c394dfe88d9df74b3edd372",
1482
+ "7ddb099532f44b8fbc77bfd94232ff8f",
1483
+ "ba7928e2ec0e459e870e7f8420fc26f6",
1484
+ "2422979a81b0425fa82df01a1b4b170a",
1485
+ "93694c9083bc4a19a51b854829180dc9",
1486
+ "8d3e47bf8478457d9db128a9927fc568",
1487
+ "6d93d4f16f7f409d895c928f4c091619",
1488
+ "bdd1de9927bf4183a39c4eb417b4ee65",
1489
+ "ed4e47b387a347f98567e87f4dce2dff",
1490
+ "95b6f6c28acc4ff3a7da7d1ac5d1fc2d",
1491
+ "dcda5b897d8c482f8ce32387af5fdb2b",
1492
+ "b2da78bd47b144f49a8202289cc6745a",
1493
+ "1ed7259217474bcfa1e5f80071fb708e",
1494
+ "2589e545cc864d4095becc8d1f75f263",
1495
+ "c1584c81502c471da4c9d89c3e922813",
1496
+ "fd455eaad05b4614acaee95e03a44fa0",
1497
+ "2df5051a2bc743258ef138f14173ccc2",
1498
+ "e2ef3cf0e3ff4ea3a8a0dff3dd73a5f1",
1499
+ "7bac50c73a644c9f9e3369b763cb5db7",
1500
+ "24206922f4c64c8aadbaec122804aadf",
1501
+ "c1f30aa01b434b0d8f9799503d9601f9",
1502
+ "7b671494c6754864931f43c546578dcb",
1503
+ "698b1bbf0fdc47e389e9d8eb5aca93d6",
1504
+ "82a61b5594dd49f2b1ca5dea552b8d87",
1505
+ "be75f5be99c246d5a01186a17181a3c3",
1506
+ "80300b7040c349ed92ecefb4d3402a7b",
1507
+ "0a1996f8fe29482aa0b972c07040d97d",
1508
+ "fa4c605349df4638a8c71e7aa52db1ad"
1509
+ ]
1510
+ },
1511
+ "outputId": "877c8003-7541-4eb2-bfd5-92540f2d2381"
1512
+ },
1513
+ "execution_count": 16,
1514
+ "outputs": [
1515
+ {
1516
+ "output_type": "stream",
1517
+ "name": "stderr",
1518
+ "text": [
1519
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
1520
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
1521
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
1522
+ "You will be able to reuse this secret in all of your notebooks.\n",
1523
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
1524
+ " warnings.warn(\n"
1525
+ ]
1526
+ },
1527
+ {
1528
+ "output_type": "display_data",
1529
+ "data": {
1530
+ "text/plain": [
1531
+ "README.md: 0%| | 0.00/360 [00:00<?, ?B/s]"
1532
+ ],
1533
+ "application/vnd.jupyter.widget-view+json": {
1534
+ "version_major": 2,
1535
+ "version_minor": 0,
1536
+ "model_id": "c46dc091acd34be2887c59bf95838529"
1537
+ }
1538
+ },
1539
+ "metadata": {}
1540
+ },
1541
+ {
1542
+ "output_type": "display_data",
1543
+ "data": {
1544
+ "text/plain": [
1545
+ "train-00000-of-00001.parquet: 0%| | 0.00/307M [00:00<?, ?B/s]"
1546
+ ],
1547
+ "application/vnd.jupyter.widget-view+json": {
1548
+ "version_major": 2,
1549
+ "version_minor": 0,
1550
+ "model_id": "6d93d4f16f7f409d895c928f4c091619"
1551
+ }
1552
+ },
1553
+ "metadata": {}
1554
+ },
1555
+ {
1556
+ "output_type": "display_data",
1557
+ "data": {
1558
+ "text/plain": [
1559
+ "Generating train split: 0%| | 0/100 [00:00<?, ? examples/s]"
1560
+ ],
1561
+ "application/vnd.jupyter.widget-view+json": {
1562
+ "version_major": 2,
1563
+ "version_minor": 0,
1564
+ "model_id": "e2ef3cf0e3ff4ea3a8a0dff3dd73a5f1"
1565
+ }
1566
+ },
1567
+ "metadata": {}
1568
+ }
1569
+ ]
1570
+ },
1571
+ {
1572
+ "cell_type": "code",
1573
+ "source": [
1574
+ "lat_mean = 39.9517411499467\n",
1575
+ "lat_std = 0.0006914493505038013\n",
1576
+ "lon_mean = -75.19143213125122\n",
1577
+ "lon_std = 0.0006539239061573955\n",
1578
+ "\n",
1579
+ "test_dataset = GPSImageDataset(\n",
1580
+ " hf_dataset=dataset_test['train'],\n",
1581
+ " transform=inference_transform,\n",
1582
+ " lat_mean=lat_mean,\n",
1583
+ " lat_std=lat_std,\n",
1584
+ " lon_mean=lon_mean,\n",
1585
+ " lon_std=lon_std\n",
1586
+ ")\n",
1587
+ "\n",
1588
+ "test_dataloader = DataLoader(\n",
1589
+ " test_dataset,\n",
1590
+ " batch_size=32,\n",
1591
+ " shuffle=False,\n",
1592
+ " num_workers=4\n",
1593
+ ")"
1594
+ ],
1595
+ "metadata": {
1596
+ "id": "S2nsXhmOZTiS"
1597
+ },
1598
+ "execution_count": 41,
1599
+ "outputs": []
1600
+ },
1601
+ {
1602
+ "cell_type": "markdown",
1603
+ "source": [
1604
+ "# Loading Our Model from Pickle File"
1605
+ ],
1606
+ "metadata": {
1607
+ "id": "VOYuBGqYZUKR"
1608
+ }
1609
+ },
1610
+ {
1611
+ "cell_type": "code",
1612
+ "source": [
1613
+ "pickle_file_path = hf_hub_download(repo_id= \"CIS-5190-CIA/Ensemble_Version_2\", filename=\"ensemble_model_ver2.pkl\")"
1614
+ ],
1615
+ "metadata": {
1616
+ "id": "ELSgBmAGZaUJ"
1617
+ },
1618
+ "execution_count": 34,
1619
+ "outputs": []
1620
+ },
1621
+ {
1622
+ "cell_type": "code",
1623
+ "source": [
1624
+ "def load_ensemble(file_name, model_classes, device=\"cpu\"):\n",
1625
+ " \"\"\"\n",
1626
+ " Load the ensemble model and individual model weights from a pickle file.\n",
1627
+ "\n",
1628
+ " Args:\n",
1629
+ " file_name: Path to the saved pickle file.\n",
1630
+ " model_classes: A dictionary mapping model names to their classes (e.g., {\"Model1\": Model1, ...}).\n",
1631
+ " device: Device to load the models onto (default is \"cpu\").\n",
1632
+ "\n",
1633
+ " Returns:\n",
1634
+ " trained_models: A dictionary of reloaded models (key -> list of models for each type).\n",
1635
+ " ensemble_weights: Numpy array of ensemble weights.\n",
1636
+ " \"\"\"\n",
1637
+ " # Load the pickle file\n",
1638
+ " with open(file_name, \"rb\") as f:\n",
1639
+ " ensemble_data = pickle.load(f)\n",
1640
+ "\n",
1641
+ " # Extract the ensemble weights\n",
1642
+ " ensemble_weights = ensemble_data[\"ensemble_weights\"]\n",
1643
+ "\n",
1644
+ " # Reload the individual models\n",
1645
+ " trained_models = {}\n",
1646
+ " for model_name, state_dicts in ensemble_data[\"models\"].items():\n",
1647
+ " trained_models[model_name] = []\n",
1648
+ " for state_dict in state_dicts:\n",
1649
+ " model = model_classes[model_name]()\n",
1650
+ " model.load_state_dict(state_dict)\n",
1651
+ " model = model.to(device)\n",
1652
+ " trained_models[model_name].append(model)\n",
1653
+ "\n",
1654
+ " return trained_models, ensemble_weights"
1655
+ ],
1656
+ "metadata": {
1657
+ "id": "1PygE9aMZ4xm"
1658
+ },
1659
+ "execution_count": 43,
1660
+ "outputs": []
1661
+ },
1662
+ {
1663
+ "cell_type": "code",
1664
+ "source": [
1665
+ "model_classes = {\n",
1666
+ " \"Model1\": lambda: Model1(dropout=0.5),\n",
1667
+ " \"Model2\": lambda: Model2(num_blocks=3, dropout_rate=0.5),\n",
1668
+ " \"Model4\": lambda: Model4(dropout_rate=0.5)\n",
1669
+ "}\n",
1670
+ "\n",
1671
+ "# Load the ensemble\n",
1672
+ "trained_models, ensemble_weights = load_ensemble(pickle_file_path, model_classes, device=\"cuda\")\n",
1673
+ "models_ensemble = []\n",
1674
+ "for model_list in trained_models.values():\n",
1675
+ " models_ensemble.extend(model_list)\n",
1676
+ "\n",
1677
+ "# ensemble model\n",
1678
+ "ensemble_model = EnsembleModel(models=models_ensemble, num_models=len(models_ensemble))\n",
1679
+ "ensemble_model.weights.data = torch.tensor(ensemble_weights, dtype=torch.float32, device=\"cuda\")\n",
1680
+ "ensemble_model = ensemble_model.to(\"cuda\")"
1681
+ ],
1682
+ "metadata": {
1683
+ "id": "WpGJ4SIrZ9G2"
1684
+ },
1685
+ "execution_count": 44,
1686
+ "outputs": []
1687
+ },
1688
+ {
1689
+ "cell_type": "markdown",
1690
+ "source": [
1691
+ "# Evaluation"
1692
+ ],
1693
+ "metadata": {
1694
+ "id": "PN94YVq0dMX1"
1695
+ }
1696
+ },
1697
+ {
1698
+ "cell_type": "code",
1699
+ "source": [
1700
+ "def evaluate_final_rmse(ensemble_model, data_loader, lat_mean, lon_mean, lat_std, lon_std):\n",
1701
+ " \"\"\"\n",
1702
+ " Evaluate the ensemble model on a given dataset and compute final RMSE in meters.\n",
1703
+ " \"\"\"\n",
1704
+ " ensemble_model.eval()\n",
1705
+ " total_loss = 0.0\n",
1706
+ " total_samples = 0\n",
1707
+ "\n",
1708
+ " with torch.no_grad():\n",
1709
+ " for images, targets in data_loader:\n",
1710
+ " images, targets = images.to(device), targets.to(device)\n",
1711
+ " outputs = ensemble_model(images)\n",
1712
+ " preds_denorm = outputs.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
1713
+ " actuals_denorm = targets.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
1714
+ "\n",
1715
+ " for pred, actual in zip(preds_denorm, actuals_denorm):\n",
1716
+ " distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters\n",
1717
+ " total_loss += distance ** 2\n",
1718
+ " total_samples += targets.size(0)\n",
1719
+ "\n",
1720
+ " final_loss = total_loss / total_samples\n",
1721
+ " final_rmse = np.sqrt(final_loss)\n",
1722
+ "\n",
1723
+ " return final_loss, final_rmse"
1724
+ ],
1725
+ "metadata": {
1726
+ "id": "zUhrqOv5cNag"
1727
+ },
1728
+ "execution_count": 47,
1729
+ "outputs": []
1730
+ },
1731
+ {
1732
+ "cell_type": "code",
1733
+ "source": [
1734
+ "final_test_loss, final_test_rmse = evaluate_final_rmse(\n",
1735
+ " ensemble_model=ensemble_model,\n",
1736
+ " data_loader=test_dataloader,\n",
1737
+ " lat_mean=lat_mean,\n",
1738
+ " lon_mean=lon_mean,\n",
1739
+ " lat_std=lat_std,\n",
1740
+ " lon_std=lon_std\n",
1741
+ ")\n",
1742
+ "\n",
1743
+ "print(f\"Test Loss (meters^2): {final_test_loss:.2f}\")\n",
1744
+ "print(f\"Test RMSE (meters): {final_test_rmse:.2f}\")"
1745
+ ],
1746
+ "metadata": {
1747
+ "colab": {
1748
+ "base_uri": "https://localhost:8080/"
1749
+ },
1750
+ "id": "-UZcLgmBcM-q",
1751
+ "outputId": "5ed71053-5017-48e5-d9ec-825ca01a8124"
1752
+ },
1753
+ "execution_count": 48,
1754
+ "outputs": [
1755
+ {
1756
+ "output_type": "stream",
1757
+ "name": "stdout",
1758
+ "text": [
1759
+ "Test Loss (meters^2): 8089.13\n",
1760
+ "Test RMSE (meters): 89.94\n"
1761
+ ]
1762
+ }
1763
+ ]
1764
+ },
1765
+ {
1766
+ "cell_type": "markdown",
1767
+ "source": [
1768
+ "# Visualizatoin"
1769
+ ],
1770
+ "metadata": {
1771
+ "id": "C-7gft4ddTzo"
1772
+ }
1773
+ },
1774
+ {
1775
+ "cell_type": "code",
1776
+ "source": [
1777
+ "def visualize_predictions(all_preds, all_actuals, lat_mean, lon_mean, lat_std, lon_std):\n",
1778
+ " \"\"\"\n",
1779
+ " Visualizes actual and predicted GPS coordinates on a scatter plot,\n",
1780
+ " including error lines connecting each prediction to its corresponding actual point.\n",
1781
+ " \"\"\"\n",
1782
+ "\n",
1783
+ " all_preds_denorm = all_preds * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
1784
+ " all_actuals_denorm = all_actuals * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
1785
+ "\n",
1786
+ " plt.figure(figsize=(10, 5))\n",
1787
+ "\n",
1788
+ " plt.scatter(all_actuals_denorm[:, 1], all_actuals_denorm[:, 0], label='Actual', color='blue', alpha=0.6)\n",
1789
+ " plt.scatter(all_preds_denorm[:, 1], all_preds_denorm[:, 0], label='Predicted', color='red', alpha=0.6)\n",
1790
+ " for i in range(len(all_actuals_denorm)):\n",
1791
+ " plt.plot(\n",
1792
+ " [all_actuals_denorm[i, 1], all_preds_denorm[i, 1]],\n",
1793
+ " [all_actuals_denorm[i, 0], all_preds_denorm[i, 0]],\n",
1794
+ " color='gray', linewidth=0.5\n",
1795
+ " )\n",
1796
+ "\n",
1797
+ " plt.legend()\n",
1798
+ " plt.xlabel('Longitude')\n",
1799
+ " plt.ylabel('Latitude')\n",
1800
+ " plt.title('Actual vs. Predicted GPS Coordinates with Error Lines')\n",
1801
+ " plt.grid(True)\n",
1802
+ " plt.show()"
1803
+ ],
1804
+ "metadata": {
1805
+ "id": "W1O4anKmd1o7"
1806
+ },
1807
+ "execution_count": 49,
1808
+ "outputs": []
1809
+ },
1810
+ {
1811
+ "cell_type": "code",
1812
+ "source": [
1813
+ "ensemble_model.eval()\n",
1814
+ "\n",
1815
+ "all_preds = []\n",
1816
+ "all_actuals = []\n",
1817
+ "\n",
1818
+ "with torch.no_grad():\n",
1819
+ " for images, targets in test_dataloader:\n",
1820
+ " images = images.to(\"cuda\")\n",
1821
+ " targets = targets.to(\"cuda\")\n",
1822
+ "\n",
1823
+ " preds = ensemble_model(images)\n",
1824
+ "\n",
1825
+ " all_preds.append(preds.cpu().numpy())\n",
1826
+ " all_actuals.append(targets.cpu().numpy())\n",
1827
+ "\n",
1828
+ "all_preds = np.concatenate(all_preds, axis=0)\n",
1829
+ "all_actuals = np.concatenate(all_actuals, axis=0)\n",
1830
+ "\n",
1831
+ "visualize_predictions(\n",
1832
+ " all_preds=all_preds,\n",
1833
+ " all_actuals=all_actuals,\n",
1834
+ " lat_mean=lat_mean,\n",
1835
+ " lon_mean=lon_mean,\n",
1836
+ " lat_std=lat_std,\n",
1837
+ " lon_std=lon_std\n",
1838
+ ")"
1839
+ ],
1840
+ "metadata": {
1841
+ "id": "m8IiYdxJdYy_"
1842
+ },
1843
+ "execution_count": null,
1844
+ "outputs": []
1845
+ }
1846
+ ]
1847
+ }