si23traktor/neural_network/nn.py

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import datasets, models, transforms
import multiprocessing

def main():
    # Set the device to use (GPU if available, otherwise CPU)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Define data transformations
    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])
        ]),
        "validation": transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
    }

    # Set the path to your vegetable images folder
    data_dir = "neural_network/dataset/vegetables"

    # Load the dataset from the folder
    image_datasets = {x: datasets.ImageFolder(f"{data_dir}/{x}", data_transforms[x])
                      for x in ["train", "validation"]}
    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=multiprocessing.cpu_count())
                   for x in ["train", "validation"]}
    dataset_sizes = {x: len(image_datasets[x]) for x in ["train", "validation"]}
    class_names = image_datasets["train"].classes
    print(class_names)
    num_classes = len(class_names)
    print(num_classes)

    # Load a pre-trained ResNet model
    model = models.resnet18(pretrained=True)
    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, num_classes)
    model = model.to(device)

    # Define the loss function and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

    # Load the previously trained model state
    #checkpoint = torch.load("neural_network/save/trained_model.pth")
    #model.load_state_dict(checkpoint)

    # Train the model
    def train_model(model, criterion, optimizer, num_epochs=2):
        best_model_wts = None  # Initialize the variable
        best_acc = 0.0

        

        for epoch in range(num_epochs):
            print(f"Epoch {epoch+1}/{num_epochs}")
            print("-" * 10)

            for phase in ["train", "validation"]:
                if phase == "train":
                    model.train()
                else:
                    model.eval()

                running_loss = 0.0
                running_corrects = 0

                for inputs, labels in dataloaders[phase]:
                    inputs = inputs.to(device)
                    labels = 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 / dataset_sizes[phase]
                epoch_acc = running_corrects.double() / dataset_sizes[phase]

                print(f"{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}")

                if phase == "validation" and epoch_acc > best_acc:
                    best_acc = epoch_acc
                    best_model_wts = model.state_dict()

        torch.save(best_model_wts, "neural_network/save/trained_model.pth")

    # Start training
    train_model(model, criterion, optimizer, num_epochs=2)


if __name__ == '__main__':
    multiprocessing.set_start_method('spawn')  # Set start method for multiprocessing
    main()