def test_weights_scalar_handler_frozen_layers(dummy_model_factory):
model = dummy_model_factory(with_grads=True, with_frozen_layer=True)
wrapper = WeightsScalarHandler(model)
mock_logger = MagicMock(spec=NeptuneLogger)
mock_logger.log_metric = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
mock_logger.log_metric.assert_has_calls(
[
call("weights_norm/fc2/weight", y=12.0, x=5),
call("weights_norm/fc2/bias", y=math.sqrt(12.0), x=5)
],
any_order=True,
)
with pytest.raises(AssertionError):
mock_logger.log_metric.assert_has_calls(
[
call("weights_norm/fc1/weight", y=12.0, x=5),
call("weights_norm/fc1/bias", y=math.sqrt(12.0), x=5)
],
any_order=True,
)
assert mock_logger.log_metric.call_count == 2
def test_weights_scalar_handler_wrong_setup():
with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"):
WeightsScalarHandler(None)
model = MagicMock(spec=torch.nn.Module)
with pytest.raises(TypeError, match="Argument reduction should be callable"):
WeightsScalarHandler(model, reduction=123)
with pytest.raises(TypeError, match="Output of the reduction function should be a scalar"):
WeightsScalarHandler(model, reduction=lambda x: x)
wrapper = WeightsScalarHandler(model)
mock_logger = MagicMock()
mock_engine = MagicMock()
with pytest.raises(TypeError, match="Handler WeightsScalarHandler works only with NeptuneLogger"):
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
def _test(tag=None):
wrapper = WeightsScalarHandler(model, tag=tag)
mock_logger = MagicMock(spec=NeptuneLogger)
mock_logger.log_metric = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
tag_prefix = f"{tag}/" if tag else ""
assert mock_logger.log_metric.call_count == 4
mock_logger.log_metric.assert_has_calls(
[
call(tag_prefix + "weights_norm/fc1/weight", y=0.0, x=5),
call(tag_prefix + "weights_norm/fc1/bias", y=0.0, x=5),
call(tag_prefix + "weights_norm/fc2/weight", y=12.0, x=5),
call(tag_prefix + "weights_norm/fc2/bias", y=math.sqrt(12.0), x=5),
],
any_order=True,
)
def run(train_batch_size, val_batch_size, epochs, lr, momentum):
train_loader, val_loader = get_data_loaders(train_batch_size,
val_batch_size)
model = Net()
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
model.to(device) # Move model before creating optimizer
optimizer = SGD(model.parameters(), lr=lr, momentum=momentum)
criterion = nn.CrossEntropyLoss()
trainer = create_supervised_trainer(model,
optimizer,
criterion,
device=device)
trainer.logger = setup_logger("Trainer")
metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)}
train_evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device)
train_evaluator.logger = setup_logger("Train Evaluator")
validation_evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device)
validation_evaluator.logger = setup_logger("Val Evaluator")
@trainer.on(Events.EPOCH_COMPLETED)
def compute_metrics(engine):
train_evaluator.run(train_loader)
validation_evaluator.run(val_loader)
npt_logger = NeptuneLogger(
api_token="ANONYMOUS",
project_name="shared/pytorch-ignite-integration",
name="ignite-mnist-example",
params={
"train_batch_size": train_batch_size,
"val_batch_size": val_batch_size,
"epochs": epochs,
"lr": lr,
"momentum": momentum,
},
)
npt_logger.attach_output_handler(
trainer,
event_name=Events.ITERATION_COMPLETED(every=100),
tag="training",
output_transform=lambda loss: {"batchloss": loss},
)
for tag, evaluator in [("training", train_evaluator),
("validation", validation_evaluator)]:
npt_logger.attach_output_handler(
evaluator,
event_name=Events.EPOCH_COMPLETED,
tag=tag,
metric_names=["loss", "accuracy"],
global_step_transform=global_step_from_engine(trainer),
)
npt_logger.attach_opt_params_handler(
trainer,
event_name=Events.ITERATION_COMPLETED(every=100),
optimizer=optimizer)
npt_logger.attach(trainer,
log_handler=WeightsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED(every=100))
npt_logger.attach(trainer,
log_handler=GradsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED(every=100))
def score_function(engine):
return engine.state.metrics["accuracy"]
handler = Checkpoint(
{"model": model},
NeptuneSaver(npt_logger),
n_saved=2,
filename_prefix="best",
score_function=score_function,
score_name="validation_accuracy",
global_step_transform=global_step_from_engine(trainer),
)
validation_evaluator.add_event_handler(Events.COMPLETED, handler)
# kick everything off
trainer.run(train_loader, max_epochs=epochs)
npt_logger.close()