def test_output_handler_with_global_step_from_engine():
mock_another_engine = MagicMock()
mock_another_engine.state = State()
mock_another_engine.state.epoch = 10
mock_another_engine.state.output = 12.345
wrapper = OutputHandler(
"tag",
output_transform=lambda x: {"loss": x},
global_step_transform=global_step_from_engine(mock_another_engine),
)
mock_logger = MagicMock(spec=ClearMLLogger)
mock_logger.clearml_logger = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 1
mock_engine.state.output = 0.123
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.clearml_logger.report_scalar.call_count == 1
mock_logger.clearml_logger.report_scalar.assert_has_calls([
call(title="tag",
series="loss",
iteration=mock_another_engine.state.epoch,
value=mock_engine.state.output)
])
mock_another_engine.state.epoch = 11
mock_engine.state.output = 1.123
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.clearml_logger.report_scalar.call_count == 2
mock_logger.clearml_logger.report_scalar.assert_has_calls([
call(title="tag",
series="loss",
iteration=mock_another_engine.state.epoch,
value=mock_engine.state.output)
])
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)
clearml_logger = ClearMLLogger(project_name="examples", task_name="ignite")
clearml_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 metrics", train_evaluator),
("validation metrics", validation_evaluator)]:
clearml_logger.attach_output_handler(
evaluator,
event_name=Events.EPOCH_COMPLETED,
tag=tag,
metric_names=["loss", "accuracy"],
global_step_transform=global_step_from_engine(trainer),
)
clearml_logger.attach_opt_params_handler(
trainer,
event_name=Events.ITERATION_COMPLETED(every=100),
optimizer=optimizer)
clearml_logger.attach(trainer,
log_handler=WeightsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED(every=100))
clearml_logger.attach(trainer,
log_handler=WeightsHistHandler(model),
event_name=Events.EPOCH_COMPLETED(every=100))
clearml_logger.attach(trainer,
log_handler=GradsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED(every=100))
clearml_logger.attach(trainer,
log_handler=GradsHistHandler(model),
event_name=Events.EPOCH_COMPLETED(every=100))
handler = Checkpoint(
{"model": model},
ClearMLSaver(),
n_saved=1,
score_function=lambda e: e.state.metrics["accuracy"],
score_name="val_acc",
filename_prefix="best",
global_step_transform=global_step_from_engine(trainer),
)
validation_evaluator.add_event_handler(Events.EPOCH_COMPLETED, handler)
# kick everything off
trainer.run(train_loader, max_epochs=epochs)
clearml_logger.close()
