def test_log_histogram(self):
assert (
os.path.exists(get_event_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM))
is False
)
assert (
os.path.exists(get_asset_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM))
is False
)
with patch("polyaxon.tracking.run.Run._log_has_events") as log_dashboard:
self.run.log_histogram(
name="histo", values=tensor_np(shape=(1024,)), bins="auto", step=1
)
self.run.log_histogram(
name="histo", values=tensor_np(shape=(1024,)), bins="fd", step=1
)
self.run.log_histogram(
name="histo", values=tensor_np(shape=(1024,)), bins="doane", step=1
)
assert log_dashboard.call_count == 3
self.event_logger.flush()
assert (
os.path.exists(get_asset_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM))
is False
)
assert (
os.path.exists(get_event_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM))
is True
)
events_file = get_event_path(
self.run_path, kind=V1ArtifactKind.HISTOGRAM, name="histo"
)
assert os.path.exists(events_file) is True
results = V1Events.read(kind="histogram", name="histo", data=events_file)
assert len(results.df.values) == 3
def test_log_histogram(self):
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM)) is
False)
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM)) is
False)
self.run.log_histogram(name="histo",
values=tensor_np(shape=(1024, )),
bins="auto",
step=1)
self.run.log_histogram(name="histo",
values=tensor_np(shape=(1024, )),
bins="fd",
step=1)
self.run.log_histogram(name="histo",
values=tensor_np(shape=(1024, )),
bins="doane",
step=1)
self.event_logger.flush()
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM)) is
False)
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.HISTOGRAM)) is
True)
events_file = get_event_path(self.run_path,
kind=V1ArtifactKind.HISTOGRAM,
name="histo")
assert os.path.exists(events_file) is True
results = V1Events.read(kind="histogram",
name="histo",
data=events_file)
assert len(results.df.values) == 3
def test_log_data_audio(self):
assert (
os.path.exists(get_event_path(self.run_path, kind=V1ArtifactKind.AUDIO))
is False
)
assert (
os.path.exists(get_asset_path(self.run_path, kind=V1ArtifactKind.AUDIO))
is False
)
with patch("polyaxon.tracking.run.Run._log_has_events") as log_dashboard:
self.run.log_audio(name="my_audio", data=tensor_np(shape=(42,)))
assert log_dashboard.call_count == 1
self.event_logger.flush()
assert (
os.path.exists(get_asset_path(self.run_path, kind=V1ArtifactKind.AUDIO))
is True
)
assert (
os.path.exists(get_event_path(self.run_path, kind=V1ArtifactKind.AUDIO))
is True
)
events_file = get_event_path(
self.run_path, kind=V1ArtifactKind.AUDIO, name="my_audio"
)
assert os.path.exists(events_file) is True
results = V1Events.read(kind="audio", name="my_audio", data=events_file)
assert len(results.df.values) == 1
asset_file = get_asset_path(
self.run_path, kind=V1ArtifactKind.AUDIO, name="my_audio", ext="wav"
)
assert os.path.exists(asset_file) is True
def test_log_data_image(self):
assert (
os.path.exists(get_event_path(self.run_path, kind=V1ArtifactKind.IMAGE))
is False
)
assert (
os.path.exists(get_asset_path(self.run_path, kind=V1ArtifactKind.IMAGE))
is False
)
with patch("polyaxon.tracking.run.Run._log_has_events") as log_dashboard:
self.run.log_image(
name="my_image", data=tensor_np(shape=(1, 8, 8)), dataformats="CHW"
)
assert log_dashboard.call_count == 1
self.event_logger.flush()
assert (
os.path.exists(get_asset_path(self.run_path, kind=V1ArtifactKind.IMAGE))
is True
)
assert (
os.path.exists(get_event_path(self.run_path, kind=V1ArtifactKind.IMAGE))
is True
)
events_file = get_event_path(
self.run_path, kind=V1ArtifactKind.IMAGE, name="my_image"
)
assert os.path.exists(events_file) is True
results = V1Events.read(kind="image", name="my_image", data=events_file)
assert len(results.df.values) == 1
asset_file = get_asset_path(
self.run_path, kind=V1ArtifactKind.IMAGE, name="my_image", ext="png"
)
assert os.path.exists(asset_file) is True
def test_log_data_audio(self):
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.AUDIO)) is False)
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.AUDIO)) is False)
self.run.log_audio(name="my_audio", data=tensor_np(shape=(42, )))
self.event_logger.flush()
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.AUDIO)) is True)
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.AUDIO)) is True)
events_file = get_event_path(self.run_path,
kind=V1ArtifactKind.AUDIO,
name="my_audio")
assert os.path.exists(events_file) is True
results = V1Events.read(kind="audio",
name="my_audio",
data=events_file)
assert len(results.df.values) == 1
asset_file = get_asset_path(self.run_path,
kind=V1ArtifactKind.AUDIO,
name="my_audio",
ext="wav")
assert os.path.exists(asset_file) is True
def test_log_data_video(self):
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.VIDEO)) is False)
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.VIDEO)) is False)
self.run.log_video(name="my_video",
data=tensor_np(shape=(4, 3, 1, 8, 8)))
self.event_logger.flush()
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.VIDEO)) is True)
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.VIDEO)) is True)
events_file = get_event_path(self.run_path,
kind=V1ArtifactKind.VIDEO,
name="my_video")
assert os.path.exists(events_file) is True
results = V1Events.read(kind="video",
name="my_video",
data=events_file)
assert len(results.df.values) == 1
asset_file = get_asset_path(self.run_path,
kind=V1ArtifactKind.VIDEO,
name="my_video",
ext="gif")
assert os.path.exists(asset_file) is True
def test_log_image_with_boxes(self):
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is False)
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is False)
image_file = tempfile.mkdtemp() + "/file.png"
self.touch(image_file)
self.run.log_image_with_boxes(
name="my_image",
tensor_image=tensor_np(shape=(3, 32, 32)),
tensor_boxes=np.array([[10, 10, 40, 40]]),
)
self.event_logger.flush()
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is True)
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is True)
events_file = get_event_path(self.run_path,
kind=V1ArtifactKind.IMAGE,
name="my_image")
assert os.path.exists(events_file) is True
results = V1Events.read(kind="image",
name="my_image",
data=events_file)
assert len(results.df.values) == 1
asset_file = get_asset_path(self.run_path,
kind=V1ArtifactKind.IMAGE,
name="my_image")
assert os.path.exists(asset_file) is True
def test_log_data_image(self):
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is False)
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is False)
self.run.log_image(name="my_image",
data=tensor_np(shape=(1, 8, 8)),
dataformats="CHW")
self.event_logger.flush()
assert (os.path.exists(
get_asset_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is True)
assert (os.path.exists(
get_event_path(self.run_path, kind=V1ArtifactKind.IMAGE)) is True)
events_file = get_event_path(self.run_path,
kind=V1ArtifactKind.IMAGE,
name="my_image")
assert os.path.exists(events_file) is True
results = V1Events.read(kind="image",
name="my_image",
data=events_file)
assert len(results.df.values) == 1
asset_file = get_asset_path(self.run_path,
kind=V1ArtifactKind.IMAGE,
name="my_image",
ext="png")
assert os.path.exists(asset_file) is True