Spaces:
Sleeping
Sleeping
File size: 12,976 Bytes
924a4d4 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"# Check if a GPU is available\n",
"print(\"GPU Available:\", torch.cuda.is_available())\n",
"\n",
"# Check the name of the GPU\n",
"if torch.cuda.is_available():\n",
" print(\"GPU Name:\", torch.cuda.get_device_name(0))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from fastai.vision.all import *\n",
"import matplotlib.pyplot as plt\n",
"\n",
"### Data Loader\n",
"path = Path('../Spectrogram_Images')\n",
"\n",
"dls = ImageDataLoaders.from_folder(\n",
" path,\n",
" train = '.',\n",
" valid_pct=0.2, # 20% of data for validation\n",
" item_tfms = Resize(224), # Resize images to 224x224 pixels for CNN\n",
" batch_tfms=aug_transforms(mult=1.0) # Apply basic augmentations\n",
")\n",
"\n",
"# Show a batch of images to verify data loading\n",
"dls.show_batch(max_n=9, figsize=(8, 8))\n",
"\n",
"### CNN\n",
"from fastai.callback.tracker import EarlyStoppingCallback\n",
"learn = vision_learner(dls, resnet34, metrics=accuracy,wd=1e-4, cbs=[EarlyStoppingCallback(monitor='valid_loss', patience=3)])\n",
"\n",
"\n",
"\n",
"lr_steep = learn.lr_find().valley\n",
"\n",
"plt.show()\n",
"\n",
"learn.model[-1].add_module('dropout', nn.Dropout(p=0.5))\n",
"learn.fine_tune(10, base_lr=lr_steep)\n",
"\n",
"# Fine-tune with early stopping\n",
"\n",
"# Plot training and validation loss curves\n",
"learn.recorder.plot_loss()\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Results: [0.3122069239616394, 0.8672986030578613]\n",
"Validation Loss: 0.3122\n",
"Validation Accuracy: 0.8673\n"
]
},
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Loss: 0.4510\n",
"Training Accuracy: 0.8065\n",
"The model seems to be generalizing well.\n"
]
}
],
"source": [
"### CNN\n",
"from fastai.vision.all import *\n",
"from fastai.callback.tracker import EarlyStoppingCallback\n",
"path = Path('../Spectrogram_Images/s')\n",
"set_seed(31)\n",
"dls = ImageDataLoaders.from_folder(\n",
" path,\n",
" train = '.',\n",
" valid_pct=0.2, # 20% of data for validation\n",
" item_tfms = Resize(224), # Resize images to 224x224 pixels for CNN\n",
" batch_tfms=aug_transforms(mult=1.0) # Apply basic augmentations\n",
")\n",
"learn = vision_learner(dls, resnet34, metrics=accuracy, wd=1e-4 ,cbs=[EarlyStoppingCallback(monitor='valid_loss', patience=3)])\n",
"\n",
"learn.load('final_model')\n",
"learn.model.eval()\n",
"# Validate the loaded model on the validation dataset\n",
"validation_results = learn.validate(dl=dls.valid)\n",
"print(f\"Validation Results: {validation_results}\")\n",
"\n",
"# Print the validation results\n",
"validation_loss, validation_accuracy = validation_results\n",
"print(f\"Validation Loss: {validation_loss:.4f}\")\n",
"print(f\"Validation Accuracy: {validation_accuracy:.4f}\")\n",
"\n",
"# Compare with training metrics (if available)\n",
"training_results = learn.validate(dl=dls.train)\n",
"training_loss, training_accuracy = training_results\n",
"print(f\"Training Loss: {training_loss:.4f}\")\n",
"print(f\"Training Accuracy: {training_accuracy:.4f}\")\n",
"\n",
"# Check for overfitting\n",
"if training_accuracy > validation_accuracy + 0.05:\n",
" print(\"Warning: The model might be overfitting.\")\n",
"else:\n",
" print(\"The model seems to be generalizing well.\")\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predictions shape: torch.Size([848, 2])\n",
"Targets shape: torch.Size([848])\n"
]
},
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "ValueError",
"evalue": "Predictions or targets are None. Please check the DataLoader and the test data.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[16], line 25\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Check if preds or targets are None\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m targets \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPredictions or targets are None. Please check the DataLoader and the test data.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# Print the shapes of preds and targets for debugging\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPredictions shape: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpreds\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mValueError\u001b[0m: Predictions or targets are None. Please check the DataLoader and the test data."
]
}
],
"source": [
"from sklearn.metrics import classification_report\n",
"\n",
"\n",
"path = Path(\"Test\")\n",
"\n",
"learn = load_learner('Test/final_model.pkl')\n",
"\n",
"test_dl = learn.dls.test_dl(get_image_files(path))\n",
"\n",
"preds, targets =learn.get_preds(dl=dls.train)\n",
"\n",
"# Check if preds or targets are None\n",
"if preds is None or targets is None:\n",
" raise ValueError(\"Predictions or targets are None. Please check the DataLoader and the test data.\")\n",
"\n",
"# Print the shapes of preds and targets for debugging\n",
"print(f\"Predictions shape: {preds.shape}\")\n",
"print(f\"Targets shape: {targets.shape}\")\n",
"\n",
"# Get predictions and targets\n",
"preds, targets = learn.get_preds(dl=test_dl)\n",
"\n",
"# Check if preds or targets are None\n",
"if preds is None or targets is None:\n",
" raise ValueError(\"Predictions or targets are None. Please check the DataLoader and the test data.\")\n",
"\n",
"# Print the shapes of preds and targets for debugging\n",
"print(f\"Predictions shape: {preds.shape}\")\n",
"print(f\"Targets shape: {targets.shape}\")\n",
"\n",
"# Calculate accuracy\n",
"acc = accuracy(preds, targets)\n",
"print(f'Accuracy on test set: {acc.item():.4f}')\n",
"\n",
"# Plot confusion matrix\n",
"interp = ClassificationInterpretation.from_learner(learn, dl=test_dl)\n",
"interp.plot_confusion_matrix()\n",
"plt.show()\n",
"\n",
"# Print classification report\n",
"y_pred = preds.argmax(dim=1)\n",
"y_true = targets\n",
"print(classification_report(y_true, y_pred, target_names=learn.dls.vocab))\n",
"\n",
"# Print additional information\n",
"print(f\"Number of classes: {len(learn.dls.vocab)}\")\n",
"print(f\"Class names: {learn.dls.vocab}\")\n",
"print(f\"Number of test images: {len(test_dl.dataset)}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "laugh_detection",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|