Avalanche - v0.1.0
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Putting All Together
Design Your Continual Learning Experiments
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.
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!pip install avalanche-lib==0.1.0
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🛴 A Comprehensive Example
Here we report a complete example of the Avalanche usage:
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from torch.optim import SGD
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from torch.nn import CrossEntropyLoss
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from avalanche.benchmarks.classic import SplitMNIST
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from avalanche.evaluation.metrics import forgetting_metrics, accuracy_metrics, \
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loss_metrics, timing_metrics, cpu_usage_metrics, confusion_matrix_metrics, disk_usage_metrics
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from avalanche.models import SimpleMLP
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from avalanche.logging import InteractiveLogger, TextLogger, TensorboardLogger
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from avalanche.training.plugins import EvaluationPlugin
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from avalanche.training.strategies import Naive
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scenario = SplitMNIST(n_experiences=5)
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# MODEL CREATION
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model = SimpleMLP(num_classes=scenario.n_classes)
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# DEFINE THE EVALUATION PLUGIN and LOGGERS
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# The evaluation plugin manages the metrics computation.
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# It takes as argument a list of metrics, collectes their results and returns
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# them to the strategy it is attached to.
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# log to Tensorboard
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tb_logger = TensorboardLogger()
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# log to text file
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text_logger = TextLogger(open('log.txt', 'a'))
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# print to stdout
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interactive_logger = InteractiveLogger()
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eval_plugin = EvaluationPlugin(
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accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True),
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loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
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timing_metrics(epoch=True, epoch_running=True),
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forgetting_metrics(experience=True, stream=True),
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cpu_usage_metrics(experience=True),
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confusion_matrix_metrics(num_classes=scenario.n_classes, save_image=False,
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stream=True),
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disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True),
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loggers=[interactive_logger, text_logger, tb_logger]
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)
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# CREATE THE STRATEGY INSTANCE (NAIVE)
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cl_strategy = Naive(
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model, SGD(model.parameters(), lr=0.001, momentum=0.9),
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CrossEntropyLoss(), train_mb_size=500, train_epochs=1, eval_mb_size=100,
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evaluator=eval_plugin)
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# TRAINING LOOP
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print('Starting experiment...')
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results = []
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for experience in scenario.train_stream:
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print("Start of experience: ", experience.current_experience)
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print("Current Classes: ", experience.classes_in_this_experience)
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# train returns a dictionary which contains all the metric values
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res = cl_strategy.train(experience)
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print('Training completed')
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print('Computing accuracy on the whole test set')
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# test also returns a dictionary which contains all the metric values
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results.append(cl_strategy.eval(scenario.test_stream))
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🤝 Run it on Google Colab
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