Traktor/neuralnetwork.py

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from PIL import Image

class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(64 * 64 * 3, 128) # *3 aby wchodziły kolory
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)
        self.relu = nn.ReLU()

        self.log_softmax = nn.LogSoftmax(dim=1)

    def forward(self, x):
        x = x.view(x.size(0), -1)  # Spłaszczenie obrazów
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.log_softmax(self.fc3(x))
        return x

def train(model, train_loader, n_iter=100):
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001) #adam niby lepszy
    criterion = nn.NLLLoss()
    model.train()
    for epoch in range(n_iter):
        running_loss = 0.0
        for images, targets in train_loader:
            images, targets = images.to(device), targets.to(device)
            optimizer.zero_grad()
            out = model(images)
            loss = criterion(out, targets)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        if epoch % 10 == 0:
            print(f'Epoch: {epoch:3d}, Loss: {running_loss/len(train_loader):.4f}')

def predict_image(image_path, model):
    image = Image.open(image_path)
    if image.mode == 'RGBA':
        image = image.convert('RGB')
    elif image.mode != 'RGB':
        image = image.convert('RGB')

    image = transform(image).unsqueeze(0).to(device)

    class_names = ["fasola","brokul","kapusta","marchewka", "kalafior",
                   "ogorek", "ziemniak", "dynia", "rzodkiewka", "pomidor"]
    with torch.no_grad():
        output = model(image)
        _, predicted = torch.max(output, 1)
    return class_names[predicted.item()]

def accuracy(model, dataset):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, targets in DataLoader(dataset, batch_size=256):
            images, targets = images.to(device), targets.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)
            correct += (predicted == targets).sum().item()
            total += targets.size(0)
    return correct / total

def load_model(model_path):
    model = SimpleNN()
    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
    model.eval()
    return model

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
transform = transforms.Compose([
    transforms.Resize((64, 64)),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),  #random jasnosci i wywaliłem ze wszystko jest szare
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_data_path = 'train'
train_dataset = datasets.ImageFolder(root=train_data_path, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

test_data_path = 'test'
test_dataset = datasets.ImageFolder(root=train_data_path, transform=transform)

model = SimpleNN().to(device)

'''
if __name__ == "__main__":
    train(model, train_loader, n_iter=100)
    torch.save(model.state_dict(), 'model.pth')

    model.load_state_dict(torch.load('model.pth', map_location=device))

    model_accuracy = accuracy(model, test_dataset)
    print(f'Dokładność modelu: {model_accuracy * 100:.2f}%')
'''