optimizedcputrainer.py

import torch
import torch.nn as nn
from torch.optim import Adam
from torchvision import datasets, models, transforms
from torch.utils.data import DataLoader, random_split

# Path to your dataset
DATA_DIR = "samples/"

# Training settings
BATCH_SIZE = 8
EPOCHS = 5
LR = 1e-4

# Force CPU mode
device = torch.device("cpu")
print("Training on:", device)

# Image preprocessing
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

# Load dataset
dataset = datasets.ImageFolder(DATA_DIR, transform=transform)
class_names = dataset.classes
print("Classes:", class_names)

# Train/test split
train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
train_ds, test_ds = random_split(dataset, [train_size, test_size])

train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)
test_loader = DataLoader(test_ds, batch_size=BATCH_SIZE)

# Load pretrained MobileNetV2
model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.DEFAULT)

# Replace classifier head
num_features = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_features, len(class_names))

model = model.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=LR)

# Training loop
for epoch in range(EPOCHS):
    model.train()
    total_loss = 0

    for imgs, labels in train_loader:
        imgs, labels = imgs.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(imgs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        total_loss += loss.item()

    print(f"Epoch {epoch+1}/{EPOCHS} - Loss: {total_loss:.4f}")

# Evaluation
model.eval()
correct = 0
total = 0

with torch.no_grad():
    for imgs, labels in test_loader:
        imgs, labels = imgs.to(device), labels.to(device)
        outputs = model(imgs)
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f"Accuracy: {100 * correct / total:.2f}%")

# Save trained model
torch.save(model.state_dict(), "image_classifier_mobilenetv2_cpu.pth")
print("Model saved as image_classifier_mobilenetv2_cpu.pth")