Evaluation

Protocols and Metrics Code Examples
"Evaluation Pluging" Example
# --- CONFIG
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
RandomCrop(28, padding=4),
ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
test_transform = transforms.Compose([
ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# ---------
# --- SCENARIO CREATION
mnist_train = MNIST('./data/mnist', train=True,
download=True, transform=train_transform)
mnist_test = MNIST('./data/mnist', train=False,
download=True, transform=test_transform)
scenario = nc_scenario(
mnist_train, mnist_test, 5, task_labels=False, seed=1234)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
# DEFINE THE EVALUATION PLUGIN AND LOGGER
# The evaluation plugin manages the metrics computation.
# It takes as argument a list of metrics and a list of loggers.
# The evaluation plugin calls the loggers to serialize the metrics
# and save them in persistent memory or print them in the standard output.
# 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),
cpu_usage_metrics(experience=True),
ExperienceForgetting(),
StreamConfusionMatrix(num_classes=scenario.n_classes, save_image=False),
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, test_mb_size=100,
device=device, 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, num_workers=4)
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, num_workers=4))

🤝 Run it on Google Colab

You can run this chapter and play with it on Google Colaboratory:

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