def test_pnn(self):
# check that pnn reaches high enough accuracy.
# Ensure nothing weird is happening with the multiple heads.
set_deterministic_run(seed=0)
main_metric = StreamAccuracy()
exp_acc = ExperienceAccuracy()
evalp = EvaluationPlugin(main_metric, exp_acc, loggers=None)
model = PNN(num_layers=1, in_features=6, hidden_features_per_column=50)
optimizer = SGD(model.parameters(), lr=0.1)
strategy = PNNStrategy(
model,
optimizer,
train_mb_size=32,
device=get_device(),
eval_mb_size=512,
train_epochs=1,
evaluator=evalp,
)
benchmark = get_fast_benchmark(use_task_labels=True)
for train_batch_info in benchmark.train_stream:
strategy.train(train_batch_info)
strategy.eval(benchmark.train_stream[:])
print("TRAIN STREAM ACC: ", main_metric.result())
assert (sum(main_metric.result().values()) /
float(len(main_metric.result().keys())) > 0.5)
def test_pnn(self):
# only multi-task scenarios.
# eval on future tasks is not allowed.
model = PNN(num_layers=3, in_features=6, hidden_features_per_column=10)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
strategy = PNNStrategy(
model,
optimizer,
train_mb_size=10,
device=self.device,
eval_mb_size=50,
train_epochs=2,
)
# train and test loop
benchmark = self.load_benchmark(use_task_labels=True)
for train_task in benchmark.train_stream:
strategy.train(train_task)
strategy.eval(benchmark.test_stream)
def test_pnn_on_multiple_tasks(self):
model = PNN(
num_layers=2,
in_features=6,
hidden_features_per_column=10,
adapter="mlp",
)
benchmark = load_benchmark(use_task_labels=True)
d0 = benchmark.train_stream[0].dataset
mb0 = iter(DataLoader(d0)).__next__()
d1 = benchmark.train_stream[1].dataset
mb1 = iter(DataLoader(d1)).__next__()
avalanche_model_adaptation(model, d0)
avalanche_model_adaptation(model, d1)
model(mb0[0], task_labels=mb0[-1])
model(mb1[0], task_labels=mb1[-1])