8000 Issue 1124 by vfdev-5 · Pull Request #1170 · pytorch/ignite · GitHub
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Issue 1124 #1170

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Merged
merged 3 commits into from
Jul 1, 2020
Merged

Issue 1124 #1170

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f0e83a2
Fixes #1124
vfdev-5 Jun 30, 2020
686a120
Log vector as title=tag+key, series=str(index)
vfdev-5 Jul 1, 2020
852b656
Merge branch 'master' into issue-1124
vfdev-5 Jul 1, 2020
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10 changes: 8 additions & 2 deletions ignite/contrib/handlers/trains_logger.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import numbers
import os
import tempfile
import warnings
Expand Down Expand Up @@ -146,10 +147,15 @@ def __call__(self, engine, logger, event_name):
)

for key, value in metrics.items():
if isinstance(value, (float, int)):
if isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0:
logger.trains_logger.report_scalar(title=self.tag, series=key, iteration=global_step, value=value)
elif isinstance(value, torch.Tensor) and value.ndimension() == 1:
for i, v in enumerate(value):
logger.trains_logger.report_scalar(
title="{}/{}".format(self.tag, key), series=str(i), iteration=global_step, value=v.item()
)
else:
warnings.warn("TrainsLogger output_handler can not log " "metrics value type {}".format(type(value)))
warnings.warn("TrainsLogger output_handler can not log metrics value type {}".format(type(value)))


class OptimizerParamsHandler(BaseOptimizerParamsHandler):
Expand Down
20 changes: 19 additions & 1 deletion tests/ignite/contrib/handlers/test_trains_logger.py
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Expand Up @@ -119,7 +119,7 @@ def test_output_handler_metric_names(dirname):
mock_logger = MagicMock(spec=TrainsLogger)
mock_logger.trains_logger = MagicMock()

with pytest.warns(UserWarning):
with pytest.warns(UserWarning, match=r"TrainsLogger output_handler can not log metrics value type"):
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)

assert mock_logger.trains_logger.report_scalar.call_count == 1
Expand Down Expand Up @@ -147,6 +147,24 @@ def test_output_handler_metric_names(dirname):
any_order=True,
)

# log a torch vector
wrapper = OutputHandler("tag", metric_names="all")
mock_logger = MagicMock(spec=TrainsLogger)
mock_logger.trains_logger = MagicMock()

mock_engine = MagicMock()
vector = torch.tensor([0.1, 0.2, 0.1, 0.2, 0.33])
mock_engine.state = State(metrics={"vector": vector})
mock_engine.state.iteration = 5

wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)

assert mock_logger.trains_logger.report_scalar.call_count == 5
mock_logger.trains_logger.report_scalar.assert_has_calls(
[call(title="tag/vector", series=str(i), iteration=5, value=vector[i].item()) for i in range(5)],
any_order=True,
)


def test_output_handler_both(dirname):

Expand Down
0