Spaces:
Running
Running
File size: 147,475 Bytes
e09da21 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import des librairies necessaires"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from torch.utils.data import DataLoader\n",
"from torchvision.models import VGG16_Weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"transform_train = transforms.Compose([\n",
" transforms.Resize((128, 128)),\n",
" transforms.ToTensor(),\n",
" transforms.RandomVerticalFlip(),\n",
" transforms.RandomHorizontalFlip(),\n",
" transforms.RandomRotation(20),\n",
" transforms.GaussianBlur(5),\n",
" transforms.Grayscale(num_output_channels=3),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
"])\n",
"\n",
"transform_test = transforms.Compose([\n",
" transforms.Resize((128, 128)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"train_dataset = torchvision.datasets.ImageFolder(root='../dataset/Training/', transform=transform_train)\n",
"val_dataset = torchvision.datasets.ImageFolder(root='../dataset/Training/', transform=transform_test)\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\n",
"val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chargeons le modèle VGG16 pré-entraîné et modifier la dernière couche"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"vgg16 = torchvision.models.vgg16(weights=VGG16_Weights.DEFAULT)\n",
"\n",
"num_classes = 4\n",
"vgg16.classifier[6] = nn.Linear(vgg16.classifier[6].in_features, num_classes)\n",
"\n",
"torch.cuda.empty_cache()\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"vgg16 = vgg16.to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Geler les paramètres des couches convolutives"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"for param in vgg16.features.parameters():\n",
" param.requires_grad = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optimiseur"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.Adam(vgg16.classifier[6].parameters(), lr=0.001)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Entrainement"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch [1/30], Loss: 0.8480, Accuracy: 66.53%\n",
"Validation Loss: 0.6656, Validation Accuracy: 74.77%\n",
"Epoch [2/30], Loss: 0.7889, Accuracy: 70.75%\n",
"Validation Loss: 0.7698, Validation Accuracy: 73.13%\n",
"Epoch [3/30], Loss: 0.7662, Accuracy: 72.51%\n",
"Validation Loss: 0.5568, Validation Accuracy: 79.95%\n",
"Epoch [4/30], Loss: 0.7840, Accuracy: 72.57%\n",
"Validation Loss: 0.7874, Validation Accuracy: 73.70%\n",
"Epoch [5/30], Loss: 0.7629, Accuracy: 73.53%\n",
"Validation Loss: 0.6636, Validation Accuracy: 77.47%\n",
"Epoch [6/30], Loss: 0.8120, Accuracy: 72.57%\n",
"Validation Loss: 0.6472, Validation Accuracy: 77.70%\n",
"Epoch [7/30], Loss: 0.8388, Accuracy: 71.71%\n",
"Validation Loss: 0.7563, Validation Accuracy: 75.75%\n",
"Epoch [8/30], Loss: 0.8267, Accuracy: 73.06%\n",
"Validation Loss: 0.7187, Validation Accuracy: 76.65%\n",
"Epoch [9/30], Loss: 0.8306, Accuracy: 73.21%\n",
"Validation Loss: 0.6719, Validation Accuracy: 77.42%\n",
"Epoch [10/30], Loss: 0.8332, Accuracy: 72.78%\n",
"Validation Loss: 0.5570, Validation Accuracy: 80.25%\n",
"Epoch [11/30], Loss: 0.8185, Accuracy: 73.32%\n",
"Validation Loss: 0.6668, Validation Accuracy: 76.19%\n",
"Epoch [12/30], Loss: 0.8339, Accuracy: 73.16%\n",
"Validation Loss: 0.7260, Validation Accuracy: 77.01%\n",
"Epoch [13/30], Loss: 0.8297, Accuracy: 73.07%\n",
"Validation Loss: 0.6501, Validation Accuracy: 77.84%\n",
"Epoch [14/30], Loss: 0.8088, Accuracy: 73.74%\n",
"Validation Loss: 0.6018, Validation Accuracy: 78.82%\n",
"Epoch [15/30], Loss: 0.8282, Accuracy: 73.62%\n",
"Validation Loss: 0.6002, Validation Accuracy: 78.41%\n",
"Epoch [16/30], Loss: 0.8581, Accuracy: 73.51%\n",
"Validation Loss: 0.6041, Validation Accuracy: 79.55%\n",
"Epoch [17/30], Loss: 0.8307, Accuracy: 73.46%\n",
"Validation Loss: 0.5556, Validation Accuracy: 80.97%\n",
"Epoch [18/30], Loss: 0.8245, Accuracy: 73.63%\n",
"Validation Loss: 0.6669, Validation Accuracy: 78.03%\n",
"Epoch [19/30], Loss: 0.8109, Accuracy: 73.51%\n",
"Validation Loss: 0.5869, Validation Accuracy: 79.57%\n",
"Epoch [20/30], Loss: 0.8304, Accuracy: 74.18%\n",
"Validation Loss: 0.5588, Validation Accuracy: 80.30%\n",
"Epoch [21/30], Loss: 0.8809, Accuracy: 73.04%\n",
"Validation Loss: 0.6278, Validation Accuracy: 78.24%\n",
"Epoch [22/30], Loss: 0.8252, Accuracy: 73.72%\n",
"Validation Loss: 0.5677, Validation Accuracy: 80.62%\n",
"Epoch [23/30], Loss: 0.8463, Accuracy: 73.06%\n",
"Validation Loss: 0.5715, Validation Accuracy: 80.71%\n",
"Epoch [24/30], Loss: 0.8499, Accuracy: 72.06%\n",
"Validation Loss: 0.8197, Validation Accuracy: 76.17%\n",
"Epoch [25/30], Loss: 0.8130, Accuracy: 74.35%\n",
"Validation Loss: 0.6600, Validation Accuracy: 78.57%\n",
"Epoch [26/30], Loss: 0.8236, Accuracy: 74.56%\n",
"Validation Loss: 0.6913, Validation Accuracy: 78.12%\n",
"Epoch [27/30], Loss: 0.8383, Accuracy: 73.30%\n",
"Validation Loss: 0.5750, Validation Accuracy: 80.85%\n",
"Epoch [28/30], Loss: 0.8454, Accuracy: 73.23%\n",
"Validation Loss: 0.5755, Validation Accuracy: 80.46%\n",
"Epoch [29/30], Loss: 0.8464, Accuracy: 72.97%\n",
"Validation Loss: 0.5708, Validation Accuracy: 80.09%\n",
"Epoch [30/30], Loss: 0.8492, Accuracy: 73.34%\n",
"Validation Loss: 0.6334, Validation Accuracy: 79.88%\n"
]
}
],
"source": [
"num_epochs = 30\n",
"train_losses = []\n",
"train_accuracies = []\n",
"val_losses = []\n",
"val_accuracies = []\n",
"\n",
"for epoch in range(num_epochs):\n",
" vgg16.train()\n",
" running_loss = 0.0\n",
" correct = 0\n",
" total = 0\n",
" \n",
" for images, labels in train_loader:\n",
" images, labels = images.to(device), labels.to(device)\n",
" optimizer.zero_grad()\n",
" outputs = vgg16(images)\n",
" loss = criterion(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" running_loss += loss.item()\n",
" \n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += labels.size(0)\n",
" correct += (predicted == labels).sum().item()\n",
" \n",
" epoch_loss = running_loss / len(train_loader)\n",
" epoch_accuracy = 100 * correct / total\n",
" \n",
" train_losses.append(epoch_loss)\n",
" train_accuracies.append(epoch_accuracy)\n",
" \n",
" print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}, Accuracy: {epoch_accuracy:.2f}%')\n",
" \n",
" #Validation\n",
" vgg16.eval()\n",
" val_running_loss = 0.0\n",
" correct = 0\n",
" total = 0\n",
"\n",
" with torch.no_grad():\n",
" for images, labels in val_loader:\n",
" images, labels = images.to(device), labels.to(device)\n",
" outputs = vgg16(images)\n",
" loss = criterion(outputs, labels)\n",
" val_running_loss += loss.item()\n",
" \n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += labels.size(0)\n",
" correct += (predicted == labels).sum().item()\n",
" \n",
" val_epoch_loss = val_running_loss / len(val_loader)\n",
" val_epoch_accuracy = 100 * correct / total\n",
" \n",
" val_losses.append(val_epoch_loss)\n",
" val_accuracies.append(val_epoch_accuracy)\n",
" \n",
" print(f'Validation Loss: {val_epoch_loss:.4f}, Validation Accuracy: {val_epoch_accuracy:.2f}%')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualisation métrique de l'entrainemnt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"plt.figure(figsize=(12, 4))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(train_losses, label='Train Loss')\n",
"plt.plot(val_losses, label='Test Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"plt.title('Train Loss')\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(train_accuracies, label='Train Accuracy')\n",
"plt.plot(val_accuracies, label='Test Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Accuracy (%)')\n",
"plt.legend()\n",
"plt.title('Train Accuracy')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Métriques d'évaluations"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"all_preds = []\n",
"all_labels = []\n",
"all_scores = []\n",
"\n",
"vgg16.eval()\n",
"with torch.no_grad():\n",
" for images, labels in val_loader:\n",
" images, labels = images.to(device), labels.to(device)\n",
" outputs = vgg16(images)\n",
" scores = nn.functional.softmax(outputs, dim=1)\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" all_preds.extend(predicted.cpu().numpy())\n",
" all_labels.extend(labels.cpu().numpy())\n",
" all_scores.extend(scores.cpu().numpy())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matrice de confusion"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cm = confusion_matrix(all_labels, all_preds)\n",
"\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=val_dataset.classes)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.7988445378151261\n",
"Precision: 0.8024759488906987\n",
"Recall: 0.7883997216511318\n",
"F1-Score: 0.7750706638547485\n",
"ROC-AUC: 0.9512775352099148\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, roc_auc_score\n",
"\n",
"y_true = all_labels\n",
"y_pred = all_preds\n",
"\n",
"accuracy = accuracy_score(y_true, y_pred)\n",
"precision = precision_score(y_true, y_pred, average='macro')\n",
"recall = recall_score(y_true, y_pred, average='macro')\n",
"f1 = f1_score(y_true, y_pred, average='macro')\n",
"roc_auc = roc_auc_score(y_true, all_scores, multi_class='ovr')\n",
"\n",
"print(f\"Accuracy: {accuracy}\")\n",
"print(f\"Precision: {precision}\")\n",
"print(f\"Recall: {recall}\")\n",
"print(f\"F1-Score: {f1}\")\n",
"print(f\"ROC-AUC: {roc_auc}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sauvegarde du modèle"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"torch.save(vgg16.state_dict(), 'tumor_model_transfer_learning.pth')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "nlpsn",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|