def log(self, key, v):
value = deepcopy(v)
if isinstance(value, np.ndarray) and len(value.shape) == 0:
value = float(value)
elif isinstance(value, np.ndarray) and len(value.shape) == 1:
value = File.as_image(np.array([value]))
elif isinstance(value, np.ndarray) and len(value.shape) <= 3:
value = File.as_image(value)
elif isinstance(value, np.ndarray):
return
self.run[key].log(value)
def test_log_many_values(self):
exp = init(mode="debug", flush_period=0.5)
exp["some/num/val"].log([5, 10, 15])
exp["some/str/val"].log(["some text", "other"])
exp["some/img/val"].log([
FileVal.as_image(PIL.Image.new("RGB", (60, 30), color="red")),
FileVal.as_image(PIL.Image.new("RGB", (20, 90), color="red")),
])
self.assertEqual(exp["some"]["num"]["val"].fetch_last(), 15)
self.assertEqual(exp["some"]["str"]["val"].fetch_last(), "other")
self.assertIsInstance(exp.get_structure()["some"]["img"]["val"],
FileSeries)
def test_log_value_errors(self):
exp = init(mode="debug", flush_period=0.5)
img = FileVal.as_image(PIL.Image.new("RGB", (60, 30), color="red"))
with self.assertRaises(ValueError):
exp["x"].log([])
with self.assertRaises(ValueError):
exp["x"].log([5, "str"])
with self.assertRaises(ValueError):
exp["x"].log([5, 10], step=10)
exp["some/num/val"].log([5], step=1)
exp["some/num/val"].log([])
with self.assertRaises(ValueError):
exp["some/num/val"].log("str")
with self.assertRaises(TypeError):
exp["some/num/val"].log(img)
exp["some/str/val"].log(["str"], step=1)
exp["some/str/val"].log([])
exp["some/img/val"].log([img], step=1)
exp["some/img/val"].log([])
with self.assertRaises(TypeError):
exp["some/img/val"].log(5)
with self.assertRaises(FileNotFound):
exp["some/img/val"].log("path")
self.assertEqual(exp["some"]["num"]["val"].fetch_last(), 5)
self.assertEqual(exp["some"]["str"]["val"].fetch_last(), "str")
self.assertIsInstance(exp.get_structure()["some"]["img"]["val"],
FileSeries)
def handle_files_and_images(self):
# image
im_frame = Image.open(self.img_path)
g_img = File.as_image(im_frame)
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["assigned image"] = g_img
self.exp.wait()
with self.with_check_if_file_appears("assigned image.png"):
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["assigned image"].download()
with self.with_check_if_file_appears("custom_dest.png"):
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["assigned image"].download(
"custom_dest.png")
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["logged image"].log(g_img)
with open(self.img_path, mode="r") as f:
# self.exp[ARTIFACT_ATTRIBUTE_SPACE]['assigned image stream'] = f
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["logged image stream"].log(f)
# artifact
text_file = neptune.types.File(self.text_file_path)
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["assigned file"] = text_file
# self.exp[ARTIFACT_ATTRIBUTE_SPACE]['logged file'].log(text_file) # wrong type
with open(self.text_file_path, mode="r") as f:
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["assigned file stream"] = f
self.exp[ARTIFACT_ATTRIBUTE_SPACE]["logged file stream"].log(f)
def test_assign_content(self):
# given
wait = self._random_wait()
path = self._random_path()
op_processor = MagicMock()
exp = self._create_run(processor=op_processor)
attr = FileSeries(exp, path)
file = File.as_image(numpy.random.rand(10, 10) * 255)
# when
attr.assign([file], wait=wait)
# then
op_processor.enqueue_operation.assert_has_calls([
call(ClearImageLog(path), False),
call(
LogImages(
path,
[
LogImages.ValueType(
ImageValue(base64_encode(file.content), None,
None),
None,
self._now(),
)
],
),
wait,
),
])
def test_log_content(self):
# given
wait = self._random_wait()
path = self._random_path()
op_processor = MagicMock()
exp = self._create_run(processor=op_processor)
attr = FileSeries(exp, path)
file = File.as_image(numpy.random.rand(10, 10) * 255)
# when
attr.log(
file,
step=3,
timestamp=self._now(),
wait=wait,
name="nazwa",
description="opis",
)
# then
op_processor.enqueue_operation.assert_called_once_with(
LogImages(
path,
[
LogImages.ValueType(
ImageValue(base64_encode(file.content), "nazwa",
"opis"),
3,
self._now(),
)
],
),
wait,
)
def validation_step(self, batch, batch_idx):
if batch_idx == 0:
# Plot example
model_output = self(batch)
fig = plot_example(batch, model_output)
self.logger.experiment['validation/plot'].log(File.as_image(fig))
return self._training_or_validation_step(batch, is_train_step=False)
def test_log(self):
exp = init(mode="debug", flush_period=0.5)
exp["some/num/val"].log(5)
exp["some/str/val"].log("some text")
exp["some/img/val"].log(
FileVal.as_image(PIL.Image.new("RGB", (60, 30), color="red")))
exp["some/img/val"].log(PIL.Image.new("RGB", (60, 30), color="red"))
self.assertEqual(exp["some"]["num"]["val"].fetch_last(), 5)
self.assertEqual(exp["some"]["str"]["val"].fetch_last(), "some text")
self.assertIsInstance(exp.get_structure()["some"]["img"]["val"],
FileSeries)
def test_as_image(self):
# given
image_array = numpy.random.rand(10, 10) * 255
expected_image = Image.fromarray(image_array.astype(numpy.uint8))
# when
file = File.as_image(expected_image)
# then
self.assertEqual(file.extension, "png")
self.assertEqual(file.content, _get_pil_image_data(expected_image))
def validation_step(self, batch, batch_idx):
if batch_idx == 0:
# Plot example
model_output = self(batch)
fig = plot_example(batch,
model_output,
history_len=params['history_len'],
forecast_len=params['forecast_len'],
nwp_channels=params['nwp_channels'])
self.logger.experiment['validation/plot'].log(File.as_image(fig))
return self._training_or_validation_step(batch, is_train_step=False)
def plot_images_on_weblogger(dataset_name, stats, images, labels, more, log_text, weblogger=2):
plot_images = images[0:np.max((4, len(images)))]
if labels is None:
labels = np.repeat('', len(plot_images))
labels = labels[0:np.max((4, len(labels)))]
add_text = [''] * len(labels)
if isinstance(more, dict) and 'image_name' in list(more.keys()):
add_text = more['image_name']
metric_str = 'Debug/{} example images: [{}]'.format(log_text, dataset_name)
if isinstance(weblogger, neptune.run.Run):
[weblogger[metric_str].log
(File.as_image(convert_normalized_tensor_to_plottable_array(im, stats['mean'], stats['std'], text=f'{lb}' + os.path.splitext(n)[0])/255))
for im, lb, n in zip(plot_images, labels, add_text)]
def log_series(self):
# floats
self.exp[LOG_ATTRIBUTE_SPACE]["m1"].log(1)
self.exp[LOG_ATTRIBUTE_SPACE]["m1"].log(2)
self.exp[LOG_ATTRIBUTE_SPACE]["m1"].log(3)
self.exp[LOG_ATTRIBUTE_SPACE]["m1"].log(2)
self.exp[LOG_ATTRIBUTE_SPACE]["nested"]["m1"].log(1)
# texts
self.exp[LOG_ATTRIBUTE_SPACE]["m2"].log("a")
self.exp[LOG_ATTRIBUTE_SPACE]["m2"].log("b")
self.exp[LOG_ATTRIBUTE_SPACE]["m2"].log("c")
# images
im_frame = Image.open(self.img_path)
g_img = File.as_image(im_frame)
self.exp[LOG_ATTRIBUTE_SPACE]["g_img"].log(g_img)
def test_assign_series(self):
exp = init(mode="debug", flush_period=0.5)
exp["some/num/val"].assign(FloatSeriesVal([1, 2, 0, 10]))
exp["some/str/val"].assign(StringSeriesVal(["text1", "text2"]),
wait=True)
exp["some/img/val"].assign(
FileSeriesVal([
FileVal.as_image(PIL.Image.new("RGB", (10, 15), color="red"))
]))
self.assertEqual(exp["some"]["num"]["val"].fetch_last(), 10)
self.assertEqual(exp["some"]["str"]["val"].fetch_last(), "text2")
self.assertIsInstance(exp.get_structure()["some"]["img"]["val"],
FileSeries)
exp["some/num/val"].assign(FloatSeriesVal([122, 543, 2, 5]))
exp["some/str/val"].assign(StringSeriesVal(
["other 1", "other 2", "other 3"]),
wait=True)
self.assertEqual(exp["some"]["num"]["val"].fetch_last(), 5)
self.assertEqual(exp["some"]["str"]["val"].fetch_last(), "other 3")
def test_log_path(self):
# given
wait = self._random_wait()
path = self._random_path()
op_processor = MagicMock()
exp = self._create_run(processor=op_processor)
attr = FileSeries(exp, path)
file = File.as_image(numpy.random.rand(10, 10) * 255)
with create_file(file.content, binary_mode=True) as tmp_filename:
saved_file = File(tmp_filename)
# when
attr.log(
saved_file,
step=3,
timestamp=self._now(),
wait=wait,
description="something",
)
# then
op_processor.enqueue_operation.assert_called_once_with(
LogImages(
path,
[
LogImages.ValueType(
ImageValue(base64_encode(file.content), None,
"something"),
3,
self._now(),
)
],
),
wait,
)