 Here is a simplified Python code example using the Avalanche library to train and evaluate a ResNet18 model on the CLEAR benchmark. Please note that this is a basic example and might need adjustments based on your specific requirements.

```python
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from avalanche.benchmarks import clear
from avalanche.model_wrappers import PretrainedModelWrapper
from avalanche.utils.metrics import Accuracy
from avalanche.utils.progress_bars import ProgressBar
from avalanche.utils.tensorboard_logs import TensorboardLogger

# Define hyperparameters
BATCH_SIZE = 64
EPOCHS = 10
LEARNING_RATE = 0.01
MOMENTUM = 0.9
WEIGHT_DECAY = 5e-4

# Create learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

def main():
    # Initialize ResNet18 model
    model = PretrainedModelWrapper(nn.ResNet(num_classes=10))

    # Define normalization and transformation operations for training and testing data
    train_transform = transforms.Compose([
        transforms.RandomHorizontalFlip(),
        transforms.RandomResizedCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    test_transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    # Set up logging to Tensorboard, a text file, and stdout
    tb_logger = TensorboardLogger('runs/resnet18_clear')
    log_file = 'training.log'
    stdout_logger = ProgressBar(persist=True)

    # Define evaluation plugin with various metrics
    eval_metrics = [Accuracy()]

    # Set a seed value and create a CLEAR benchmark
    clear.test_set_random_seeds_and_align()
    benchmark = clear.CLEARBenchmark(num_tasks=10, num_experiences=5)

    # Move the model to the appropriate device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)

    # Define SGD optimizer
    optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM, weight_decay=WEIGHT_DECAY)
    check_model_and_optimizer(model, optimizer)

    # Create a continual learning strategy using the Naive method from Avalanche
    strategy = clear.NaiveStrategy()

    # Run a training loop, saving the model after each experience and evaluating it on the test stream
    for experience_idx, (train_data, test_data) in enumerate(benchmark):
        train_loader = torch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)
        test_loader = torch.utils.data.DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=False)

        for epoch in range(EPOCHS):
            running_loss = 0.0
            for i, (inputs, labels) in enumerate(train_loader):
                inputs, labels = inputs.to(device), labels.to(device)
                optimizer.zero_grad()
                outputs = model(inputs)
                loss = torch.nn.functional.cross_entropy(outputs, labels)
                loss.backward()
                optimizer.step()
                running_loss += loss.item()

            print(f'Epoch [{epoch+1}/{EPOCHS}], Loss: {running_loss/len(train_loader)}')
            lr_scheduler.step()

            # Evaluate the model on the test stream
            _, test_outputs = zip(*[model(test_inputs.to(device)) for test_inputs in test_loader])
            test_outputs = torch.cat(test_outputs)
            test_labels = torch.cat([torch.stack(test_labels_) for test_labels_ in test_loader.samplers.values()])
            _, preds = torch.max(test_outputs, 1)
            accuracy = Accuracy()(preds, test_labels)
            _update_metrics_and_loggers(accuracy, tb_logger, log_file, stdout_logger)

            # Save the model after each experience
            torch.save(model.state_dict(), f'models/resnet18_clear_{experience_idx}.pt')

    # Generate an accuracy matrix and compute the CLEAR metrics, logging these results to a text file
    accuracy_matrix, clear_metrics = benchmark.compute_metrics()
    _update_metrics_and_loggers(clear_metrics, tb_logger, log_file, stdout_logger)

if __name__ == '__main__':
    main()
```

This code assumes that you have already installed the Avalanche library and Torchvision. To install them, run:

```
pip install avalanche-cv torchvision
```