Putting All Together

Welcome to the "Putting All Together" tutorial of the "From Zero to Hero" series. In this part we will summarize the major Avalanche features and how you can put them together for your continual learning experiments.
!pip install avalanche-lib==0.2.1
🛴 A Comprehensive Example
Here we report a complete example of the Avalanche usage:
from torch.optim import SGD
from torch.nn import CrossEntropyLoss
from avalanche.benchmarks.classic import SplitMNIST
from avalanche.evaluation.metrics import forgetting_metrics, accuracy_metrics, \
    loss_metrics, timing_metrics, cpu_usage_metrics, confusion_matrix_metrics, disk_usage_metrics
from avalanche.models import SimpleMLP
from avalanche.logging import InteractiveLogger, TextLogger, TensorboardLogger
from avalanche.training.plugins import EvaluationPlugin
from avalanche.training.supervised import Naive
​
scenario = SplitMNIST(n_experiences=5)
​
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
​
# DEFINE THE EVALUATION PLUGIN and LOGGERS
# The evaluation plugin manages the metrics computation.
# It takes as argument a list of metrics, collectes their results and returns
# them to the strategy it is attached to.
​
# log to Tensorboard
tb_logger = TensorboardLogger()
​
# log to text file
text_logger = TextLogger(open('log.txt', 'a'))
​
# print to stdout
interactive_logger = InteractiveLogger()
​
eval_plugin = EvaluationPlugin(
    accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    timing_metrics(epoch=True, epoch_running=True),
    forgetting_metrics(experience=True, stream=True),
    cpu_usage_metrics(experience=True),
    confusion_matrix_metrics(num_classes=scenario.n_classes, save_image=False,
                             stream=True),
    disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    loggers=[interactive_logger, text_logger, tb_logger]
)
​
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(
    model, SGD(model.parameters(), lr=0.001, momentum=0.9),
    CrossEntropyLoss(), train_mb_size=500, train_epochs=1, eval_mb_size=100,
    evaluator=eval_plugin)
​
# TRAINING LOOP
print('Starting experiment...')
results = []
for experience in scenario.train_stream:
    print("Start of experience: ", experience.current_experience)
    print("Current Classes: ", experience.classes_in_this_experience)
​
    # train returns a dictionary which contains all the metric values
    res = cl_strategy.train(experience)
    print('Training completed')
​
    print('Computing accuracy on the whole test set')
    # test also returns a dictionary which contains all the metric values
    results.append(cl_strategy.eval(scenario.test_stream))
🤝 Run it on Google Colab
You can run this chapter and play with it on Google Colaboratory: ​
​​

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Extending Avalanche

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🛴 A Comprehensive Example

🤝 Run it on Google Colab