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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-01-27 16:42:47.893272: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1737974567.952170 6163 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1737974567.970030 6163 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2025-01-27 16:42:48.097247: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"source": [
"import os\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import DataLoader\n",
"from torchvision import transforms\n",
"from torchvision.datasets import ImageFolder\n",
"from transformers import ViTForImageClassification, ViTFeatureExtractor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using device: cuda\n"
]
}
],
"source": [
"# Define device\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"print(f\"Using device: {device}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Paths\n",
"dataset_path = \"/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/FACE_CROP\" # Replace with your dataset path\n",
"model_save_path = \"/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/v1.pth\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224 and are newly initialized because the shapes did not match:\n",
"- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([1364]) in the model instantiated\n",
"- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([1364, 768]) in the model instantiated\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"# Data Transformations with 160x160 resize\n",
"transform = transforms.Compose([\n",
" transforms.Resize((224, 224)), # Resize to 160x160 as per your dataset\n",
" transforms.RandomHorizontalFlip(),\n",
" transforms.RandomRotation(15),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n",
"])\n",
"\n",
"# Load Dataset\n",
"dataset = ImageFolder(root=dataset_path, transform=transform)\n",
"\n",
"# DataLoader (using all images)\n",
"dataloader = DataLoader(dataset, batch_size=16, shuffle=True, num_workers=4)\n",
"\n",
"# Define ViT Model\n",
"model = ViTForImageClassification.from_pretrained(\n",
" \"google/vit-base-patch16-224\", num_labels=len(dataset.classes), \n",
" ignore_mismatched_sizes=True # This will ignore size mismatch warnings\n",
")\n",
"\n",
"# Modify the classifier head to match the number of classes in your dataset\n",
"model.classifier = nn.Linear(model.config.hidden_size, len(dataset.classes))\n",
"\n",
"# Move model to device\n",
"model = model.to(device)\n",
"\n",
"# Define Optimizer, Loss Function, and Scheduler\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5, weight_decay=1e-4)\n",
"criterion = nn.CrossEntropyLoss()\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Training Function\n",
"def train_model(model, dataloader, epochs=100):\n",
" model.train()\n",
"\n",
" for epoch in range(epochs):\n",
" print(f\"Epoch {epoch + 1}/{epochs}\")\n",
" epoch_loss = 0.0\n",
"\n",
" for images, labels in dataloader:\n",
" images, labels = images.to(device), labels.to(device)\n",
"\n",
" # Forward pass\n",
" optimizer.zero_grad()\n",
" outputs = model(images).logits\n",
" loss = criterion(outputs, labels)\n",
"\n",
" # Backward pass\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" epoch_loss += loss.item()\n",
"\n",
" # Step the scheduler\n",
" scheduler.step()\n",
"\n",
" print(f\"Epoch Loss: {epoch_loss / len(dataloader):.4f}\")\n",
"\n",
" # Save the trained model\n",
" torch.save(model.state_dict(), model_save_path)\n",
" print(\"Training complete! Model saved.\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"Epoch Loss: 6.4940\n",
"Epoch 2/100\n",
"Epoch Loss: 4.4488\n",
"Epoch 3/100\n",
"Epoch Loss: 2.7864\n",
"Epoch 4/100\n",
"Epoch Loss: 1.6240\n",
"Epoch 5/100\n",
"Epoch Loss: 0.9264\n",
"Epoch 6/100\n",
"Epoch Loss: 0.5430\n",
"Epoch 7/100\n",
"Epoch Loss: 0.4927\n",
"Epoch 8/100\n",
"Epoch Loss: 0.4542\n",
"Epoch 9/100\n",
"Epoch Loss: 0.4172\n",
"Epoch 10/100\n",
"Epoch Loss: 0.3850\n",
"Epoch 11/100\n",
"Epoch Loss: 0.3539\n",
"Epoch 12/100\n",
"Epoch Loss: 0.3482\n",
"Epoch 13/100\n",
"Epoch Loss: 0.3455\n",
"Epoch 14/100\n",
"Epoch Loss: 0.3437\n",
"Epoch 15/100\n",
"Epoch Loss: 0.3408\n",
"Epoch 16/100\n",
"Epoch Loss: 0.3362\n",
"Epoch 17/100\n",
"Epoch Loss: 0.3362\n",
"Epoch 18/100\n",
"Epoch Loss: 0.3366\n",
"Epoch 19/100\n",
"Epoch Loss: 0.3359\n",
"Epoch 20/100\n",
"Epoch Loss: 0.3364\n",
"Epoch 21/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 22/100\n",
"Epoch Loss: 0.3352\n",
"Epoch 23/100\n",
"Epoch Loss: 0.3356\n",
"Epoch 24/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 25/100\n",
"Epoch Loss: 0.3343\n",
"Epoch 26/100\n",
"Epoch Loss: 0.3355\n",
"Epoch 27/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 28/100\n",
"Epoch Loss: 0.3350\n",
"Epoch 29/100\n",
"Epoch Loss: 0.3354\n",
"Epoch 30/100\n",
"Epoch Loss: 0.3350\n",
"Epoch 31/100\n",
"Epoch Loss: 0.3356\n",
"Epoch 32/100\n",
"Epoch Loss: 0.3358\n",
"Epoch 33/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 34/100\n",
"Epoch Loss: 0.3354\n",
"Epoch 35/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 36/100\n",
"Epoch Loss: 0.3352\n",
"Epoch 37/100\n",
"Epoch Loss: 0.3351\n",
"Epoch 38/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 39/100\n",
"Epoch Loss: 0.3343\n",
"Epoch 40/100\n",
"Epoch Loss: 0.3356\n",
"Epoch 41/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 42/100\n",
"Epoch Loss: 0.3348\n",
"Epoch 43/100\n",
"Epoch Loss: 0.3348\n",
"Epoch 44/100\n",
"Epoch Loss: 0.3360\n",
"Epoch 45/100\n",
"Epoch Loss: 0.3352\n",
"Epoch 46/100\n",
"Epoch Loss: 0.3344\n",
"Epoch 47/100\n",
"Epoch Loss: 0.3351\n",
"Epoch 48/100\n",
"Epoch Loss: 0.3360\n",
"Epoch 49/100\n",
"Epoch Loss: 0.3351\n",
"Epoch 50/100\n",
"Epoch Loss: 0.3348\n",
"Epoch 51/100\n",
"Epoch Loss: 0.3344\n",
"Epoch 52/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 53/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 54/100\n",
"Epoch Loss: 0.3359\n",
"Epoch 55/100\n",
"Epoch Loss: 0.3353\n",
"Epoch 56/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 57/100\n",
"Epoch Loss: 0.3355\n",
"Epoch 58/100\n",
"Epoch Loss: 0.3356\n",
"Epoch 59/100\n",
"Epoch Loss: 0.3352\n",
"Epoch 60/100\n",
"Epoch Loss: 0.3357\n",
"Epoch 61/100\n",
"Epoch Loss: 0.3359\n",
"Epoch 62/100\n",
"Epoch Loss: 0.3357\n",
"Epoch 63/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 64/100\n",
"Epoch Loss: 0.3358\n",
"Epoch 65/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 66/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 67/100\n",
"Epoch Loss: 0.3359\n",
"Epoch 68/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 69/100\n",
"Epoch Loss: 0.3338\n",
"Epoch 70/100\n",
"Epoch Loss: 0.3351\n",
"Epoch 71/100\n",
"Epoch Loss: 0.3358\n",
"Epoch 72/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 73/100\n",
"Epoch Loss: 0.3353\n",
"Epoch 74/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 75/100\n",
"Epoch Loss: 0.3344\n",
"Epoch 76/100\n",
"Epoch Loss: 0.3341\n",
"Epoch 77/100\n",
"Epoch Loss: 0.3352\n",
"Epoch 78/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 79/100\n",
"Epoch Loss: 0.3344\n",
"Epoch 80/100\n",
"Epoch Loss: 0.3350\n",
"Epoch 81/100\n",
"Epoch Loss: 0.3351\n",
"Epoch 82/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 83/100\n",
"Epoch Loss: 0.3358\n",
"Epoch 84/100\n",
"Epoch Loss: 0.3346\n",
"Epoch 85/100\n",
"Epoch Loss: 0.3351\n",
"Epoch 86/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 87/100\n",
"Epoch Loss: 0.3364\n",
"Epoch 88/100\n",
"Epoch Loss: 0.3356\n",
"Epoch 89/100\n",
"Epoch Loss: 0.3349\n",
"Epoch 90/100\n",
"Epoch Loss: 0.3347\n",
"Epoch 91/100\n",
"Epoch Loss: 0.3346\n",
"Epoch 92/100\n",
"Epoch Loss: 0.3354\n",
"Epoch 93/100\n",
"Epoch Loss: 0.3362\n",
"Epoch 94/100\n",
"Epoch Loss: 0.3344\n",
"Epoch 95/100\n",
"Epoch Loss: 0.3351\n",
"Epoch 96/100\n",
"Epoch Loss: 0.3346\n",
"Epoch 97/100\n",
"Epoch Loss: 0.3352\n",
"Epoch 98/100\n",
"Epoch Loss: 0.3343\n",
"Epoch 99/100\n",
"Epoch Loss: 0.3352\n",
"Epoch 100/100\n",
"Epoch Loss: 0.3346\n",
"Training complete! Model saved.\n"
]
}
],
"source": [
"# Train the Model\n",
"train_model(model, dataloader, epochs=100)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "minibat",
"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": "3.12.8"
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"nbformat": 4,
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