def test_logging():
with TemporaryDirectory() as tmpdir:
test_data = _get_test_data()
file_name = 'test_log'
output_dir = os.path.join(tmpdir, file_name)
net = _get_test_network()
ce_loss = loss.SoftmaxCrossEntropyLoss()
acc = mx.metric.Accuracy()
est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
train_metrics, val_metrics = est.prepare_loss_and_metrics()
logging_handler = event_handler.LoggingHandler(
file_name=file_name,
file_location=tmpdir,
train_metrics=train_metrics,
val_metrics=val_metrics)
est.fit(test_data, event_handlers=[logging_handler], epochs=3)
assert logging_handler.batch_index == 0
assert logging_handler.current_epoch == 3
assert os.path.isfile(output_dir)
def test_logging():
with TemporaryDirectory() as tmpdir:
test_data = _get_test_data()
file_name = 'test_log'
output_dir = os.path.join(tmpdir, file_name)
net = _get_test_network()
ce_loss = loss.SoftmaxCrossEntropyLoss()
acc = mx.gluon.metric.Accuracy()
est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
est.logger.addHandler(logging.FileHandler(output_dir))
train_metrics = est.train_metrics
val_metrics = est.val_metrics
logging_handler = event_handler.LoggingHandler(metrics=train_metrics)
est.fit(test_data, event_handlers=[logging_handler], epochs=3)
assert logging_handler.batch_index == 0
assert logging_handler.current_epoch == 3
assert os.path.isfile(output_dir)
del est # Clean up estimator and logger before deleting tmpdir