ium_464915/train.py

import torch
import mlflow
import torch.nn.functional as F

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


class DeviceDataLoader():
    def __init__(self, dl, device):
        self.dl = dl
        self.device = device
        
    def __iter__(self):
        for b in self.dl: 
            yield to_device(b, self.device)

    def __len__(self):
        return len(self.dl)


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, 1)
        self.batchnorm1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.batchnorm2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, 3, 1)
        self.fc1 = nn.Linear(128*26*26, 128)
        self.fc2 = nn.Linear(128, 2)

    def forward(self, x):
        x = F.relu(self.batchnorm1(self.conv1(x)))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.batchnorm2(self.conv2(x)))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 128*26*26)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        
        return F.log_softmax(x, dim=1)


def get_data(IMG_SIZE: int, BATCH_SIZE: int, device: torch.device):
    transformer = transforms.Compose([
        transforms.RandomRotation(20),
        transforms.RandomHorizontalFlip(p=0.3),
        transforms.RandomVerticalFlip(p=0.3),

        transforms.Resize(size = (IMG_SIZE, IMG_SIZE), antialias = True),
        transforms.CenterCrop(IMG_SIZE),

        transforms.ToTensor(),
        transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]),
    ])

    trainData = datasets.ImageFolder(root = "./train", transform = transformer)

    trainSet, valSet = torch.utils.data.random_split(trainData, \
        [int(0.8 * len(trainData)), len(trainData) - int(0.8 * len(trainData))])

    trainLoader = DataLoader(trainSet, batch_size=BATCH_SIZE, shuffle=True)
    valLoader = DataLoader(valSet, batch_size=BATCH_SIZE, shuffle=True)

    train_loader = DeviceDataLoader(trainLoader, device)
    val_loader = DeviceDataLoader(valLoader, device)
    
    return train_loader, val_loader, int(len(trainSet))


def train(EPOCHS: int, BATCH_SIZE: int, 
          model: nn.Module, train_loader: DataLoader, 
          val_loader: DataLoader, criterion: nn.Module, 
          optimizer: torch.optim.Optimizer, train_size: int):
    
    for epoch in range(EPOCHS): 
        print()
        print(f'EPOCH {epoch+1}') 
        print()

        model.train(True)

        running_loss, last_loss, avg_loss = 0., 0., 0.
        train_correct, train_total = 0, 0
        
        for i, data in enumerate(train_loader):
            input, label = data
            optimizer.zero_grad()

            output = model(input)
            loss = criterion(output, label)

            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            avg_loss += loss.item()

            if i % 10 == 0:
                last_loss = running_loss / 5
                print(f'Batch {i} Loss train: {last_loss:.3f}')
                running_loss = 0.
            
            _, predicted = torch.max(output.data, 1)
            train_total += label.size(0)
            train_correct += (predicted == label).sum().item()

        avg_loss /= train_size/BATCH_SIZE
        
        running_vloss = 0.
        
        model.eval()

        val_correct, val_total = 0, 0

        with torch.no_grad():
            for i, val_data in enumerate(val_loader):
                val_input, val_label = val_data
                val_output = model(val_input)
                val_loss = criterion(val_output, val_label)
                running_vloss += val_loss.item()

                _, vpredicted = torch.max(val_output.data, 1)
                val_total += val_label.size(0)
                val_correct += (vpredicted == val_label).sum().item()

        avg_vloss = running_vloss / (i + 1)

        train_accuracy = train_correct / train_total
        val_accuracy = val_correct / val_total

        mlflow.log_metric("train_loss", avg_loss, step=epoch)
        mlflow.log_metric("val_loss", avg_vloss, step=epoch)

        print(f'Loss train {avg_loss:.3f}, loss valid {avg_vloss:.3f}')
        print(f'Accuracy train {train_accuracy:.2%}, accuracy valid {val_accuracy:.2%}')
    
    print('Finished Training')


def to_device(data, device: torch.device):
    if isinstance(data, (list,tuple)):
        return [to_device(x, device) for x in data]
    return data.to(device, non_blocking=True)


if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    IMG_SIZE = 224
    BATCH_SIZE = 32
    EPOCHS = 3
    LEARNING_RATE = 0.001

    mlflow.set_experiment("Skin cancer classification, custom CNN model")
    mlflow.start_run()

    mlflow.log_param("IMG_SIZE", IMG_SIZE)
    mlflow.log_param("BATCH_SIZE", BATCH_SIZE)
    mlflow.log_param("EPOCHS", EPOCHS)
    mlflow.log_param("LEARNING_RATE", LEARNING_RATE)

    train_loader, val_loader, train_size = get_data(IMG_SIZE, BATCH_SIZE, device)

    model = Model()
    to_device(model, device)

    criterion= nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)

    train(EPOCHS, BATCH_SIZE, model, train_loader, 
          val_loader, criterion, optimizer, train_size)

    torch.save(model.state_dict(), "model.pth")
    mlflow.log_artifact("model.pth")