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6bf9a0e | 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 | import os
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, models, transforms
# Configuration and Paths
DATA_DIR = 'dataset'
NUM_CLASSES = 10
BATCH_SIZE = 32
NUM_EPOCHS = 10
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Running on device: {DEVICE}")
# Data Pipeline and Preprocessing
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
image_datasets = {
x: datasets.ImageFolder(os.path.join(DATA_DIR, x), data_transforms[x])
for x in ['train', 'val']
}
dataloaders = {
x: torch.utils.data.DataLoader(image_datasets[x], batch_size=BATCH_SIZE, shuffle=True)
for x in ['train', 'val']
}
class_names = image_datasets['train'].classes
print(f"Found {len(class_names)} classes: {class_names}")
# Load Pretrained EfficientNet-B0 and Modify Classifier
try:
weights = models.EfficientNet_B0_Weights.DEFAULT
model = models.efficientnet_b0(weights=weights)
except AttributeError:
model = models.efficientnet_b0(pretrained=True)
num_ftrs = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_ftrs, NUM_CLASSES)
model = model.to(DEVICE)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training Loop
best_acc = 0.0
start_time = time.time()
for epoch in range(NUM_EPOCHS):
print(f'\nEpoch {epoch+1}/{NUM_EPOCHS}')
print('-' * 15)
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
for inputs, labels in dataloaders[phase]:
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(image_datasets[phase])
epoch_acc = running_corrects.double() / len(image_datasets[phase])
print(f'[{phase.upper()}] Loss: {epoch_loss:.4f} | Accuracy: {epoch_acc:.4f}')
# Save the best model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
torch.save(model.state_dict(), 'best_thai_food_model.pth')
print("Best model updated and saved.")
time_elapsed = time.time() - start_time
print(f'\nTraining complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Highest Validation Accuracy: {best_acc:.4f}')
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