train_font_identifier.py

import copy
import os
import time
import torch
import torch.optim as optim
import torch.nn as nn
from torch.optim import lr_scheduler
from torchvision import datasets, models, transforms
from tqdm import tqdm

# Directory with organized font images
data_dir = './train_test_images'

# Transformations for the image data
data_transforms = transforms.Compose([
    transforms.Grayscale(num_output_channels=3), # Convert images to grayscale with 3 channels
    transforms.RandomCrop((224, 224)), # Resize images to the expected input size of the model
    transforms.ToTensor(), # Convert images to PyTorch tensors
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize with ImageNet stats
])

# Create datasets
image_datasets = {
    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms)
    for x in ['train', 'test']
}

# Create dataloaders
dataloaders = {
    'train': torch.utils.data.DataLoader(image_datasets['train'], batch_size=4, shuffle=True),
    'test': torch.utils.data.DataLoader(image_datasets['test'], batch_size=4, shuffle=True)
}

# Define the model
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)

# Modify the last fully connected layer to match the number of font classes you have
num_classes = len(image_datasets['train'].classes)
model.fc = nn.Linear(model.fc.in_features, num_classes)

# Define the loss function
criterion = torch.nn.CrossEntropyLoss()

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())

# Function to perform a training step with progress bar
def train_step(model, data_loader, criterion, optimizer):
    model.train()
    total_loss = 0
    progress_bar = tqdm(data_loader, desc='Training', leave=True)
    for inputs, targets in progress_bar:
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        progress_bar.set_postfix(loss=loss.item())
    progress_bar.close()
    return total_loss / len(data_loader)

# Function to perform a validation step with progress bar
def validate(model, data_loader, criterion):
    model.eval()
    total_loss = 0
    correct = 0
    progress_bar = tqdm(data_loader, desc='Validation', leave=False)
    with torch.no_grad():
        for inputs, targets in progress_bar:
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            total_loss += loss.item()
            _, predicted = torch.max(outputs, 1)
            correct += (predicted == targets).sum().item()
            progress_bar.set_postfix(loss=loss.item())
    progress_bar.close()
    return total_loss / len(data_loader), correct / len(data_loader.dataset)


print(image_datasets['train'].classes)


# Training loop with progress bar for epochs
num_epochs = 10  # Replace with the number of epochs you'd like to train for
for epoch in range(num_epochs):
    print(f"Epoch {epoch+1}/{num_epochs}")
    train_loss = train_step(model, dataloaders["train"], criterion, optimizer)
    val_loss, val_accuracy = validate(model, dataloaders["test"], criterion)
    print(f"Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}")

# Save the model to disk
torch.save(model.state_dict(), 'font_identifier_model.pth')