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_multihead_cumulative(self):
# check that multi-head reaches high enough accuracy.
# Ensure nothing weird is happening with the multiple heads.
set_deterministic_run(seed=0)
model = MHTestMLP(input_size=6, hidden_size=100)
criterion = CrossEntropyLoss()
optimizer = SGD(model.parameters(), lr=1)
main_metric = StreamAccuracy()
exp_acc = ExperienceAccuracy()
evalp = EvaluationPlugin(main_metric, exp_acc, loggers=None)
strategy = Cumulative(
model,
optimizer,
criterion,
train_mb_size=64,
device=get_device(),
eval_mb_size=512,
train_epochs=6,
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())) > 0.7)
def test_multihead_cumulative(self):
# check that multi-head reaches high enough accuracy.
# Ensure nothing weird is happening with the multiple heads.
model = MHTestMLP(input_size=6, hidden_size=100)
criterion = CrossEntropyLoss()
optimizer = SGD(model.parameters(), lr=1)
main_metric = StreamAccuracy()
exp_acc = ExperienceAccuracy()
evalp = EvaluationPlugin(main_metric, exp_acc, loggers=None)
strategy = Cumulative(model,
optimizer,
criterion,
train_mb_size=32,
device=get_device(),
eval_mb_size=512,
train_epochs=1,
evaluator=evalp)
scenario = get_fast_scenario(use_task_labels=True)
for train_batch_info in scenario.train_stream:
strategy.train(train_batch_info)
strategy.eval(scenario.train_stream[:])
print("TRAIN STREAM ACC: ", main_metric.result())
assert main_metric.result() > 0.7
def test_pnn(self):
# check that pnn reaches high enough accuracy.
# Ensure nothing weird is happening with the multiple heads.
main_metric = StreamAccuracy()
exp_acc = ExperienceAccuracy()
evalp = EvaluationPlugin(main_metric, exp_acc, loggers=None)
strategy = PNNStrategy(1,
6,
50,
0.1,
train_mb_size=32,
device=get_device(),
eval_mb_size=512,
train_epochs=1,
evaluator=evalp)
scenario = get_fast_scenario(use_task_labels=True)
for train_batch_info in scenario.train_stream:
strategy.train(train_batch_info)
strategy.eval(scenario.train_stream[:])
print("TRAIN STREAM ACC: ", main_metric.result())
assert main_metric.result() > 0.5