def __init__(self,
workflow,
title,
is_batch=False,
default_export_filename=''):
self.export_op = None
self._default_export_filename = default_export_filename
self.__topLevelOperator = OpMultiLaneWrapper(
OpTrackingBaseDataExport,
parent=workflow,
promotedSlotNames=set(['RawData', 'Inputs', 'RawDatasetInfo']))
extra_serial_slots = [
SerialSlot(self.topLevelOperator.SelectedPlugin),
SerialSlot(self.topLevelOperator.SelectedExportSource),
SerialDictSlot(self.topLevelOperator.AdditionalPluginArguments)
]
self._serializers = [
DataExportSerializer(self.topLevelOperator, title,
extra_serial_slots)
]
super(TrackingBaseDataExportApplet, self).__init__(workflow,
title,
isBatch=is_batch)
def __init__(
self,
workflow,
title,
is_batch: bool = False,
default_export_filename: str = "",
pluginExportFunc: Optional[PluginExportCallable] = None,
):
self.export_op = None
self._default_export_filename = default_export_filename
self.__topLevelOperator = OpMultiLaneWrapper(
OpTrackingBaseDataExport,
parent=workflow,
promotedSlotNames=set(["RawData", "Inputs", "RawDatasetInfo"]))
extra_serial_slots = [
SerialSlot(self.topLevelOperator.SelectedPlugin),
SerialSlot(self.topLevelOperator.SelectedExportSource),
SerialDictSlot(self.topLevelOperator.AdditionalPluginArguments),
]
self._serializers = [
DataExportSerializer(self.topLevelOperator, title,
extra_serial_slots)
]
self._pluginExportFunc = pluginExportFunc
super(TrackingBaseDataExportApplet, self).__init__(workflow,
title,
isBatch=is_batch)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialSlot(operator.Beta, selfdepends=True),
SerialSlot(operator.SolverName, selfdepends=True)
]
super(MulticutSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialSlot(operator.MinValue, selfdepends=True),
SerialSlot(operator.MaxValue, selfdepends=True)
]
super(ThresholdMaskingSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialSlot(operator.ScalingFactor, selfdepends=True),
SerialSlot(operator.Offset, selfdepends=True)
]
super(DeviationFromMeanSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, groupName):
self.TestSerialSlot = SerialSlot(operator.TestSlot)
self.TestMultiSerialSlot = SerialSlot(operator.TestMultiSlot)
self.TestSerialListSlot = SerialListSlot(operator.TestListSlot,
selfdepends=True)
slots = (self.TestSerialSlot, self.TestMultiSerialSlot,
self.TestSerialListSlot)
super(OpMockSerializer, self).__init__(groupName, slots)
def __init__(self, topGroupName, topLevelOperator):
slots = [
SerialSlot(topLevelOperator.PatchSize),
SerialSlot(topLevelOperator.HaloSize)
]
super(FillMissingSlicesSerializer, self).__init__(topGroupName,
slots=slots)
self._operator = topLevelOperator
def __init__(self, operator, projectFileGroupName):
slots = [SerialListSlot(operator.InputChannelIndexes, selfdepends=True),
SerialSlot(operator.WatershedPadding, selfdepends=True),
SerialSlot(operator.CacheBlockShape, selfdepends=True),
SerialSlot(operator.SeedThresholdValue, selfdepends=True),
SerialSlot(operator.MinSeedSize, selfdepends=True) ]
super(VigraWatershedViewerSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, projectFileGroupName):
self.topLevelOperator = operator
slots = [
SerialSlot(operator.ExportDirectory, default=''),
SerialSlot(operator.Format, default=ExportFormat.H5),
SerialSlot(operator.Suffix, default='_results'),
SerialDatasetPath(operator.DatasetPath,
operator.Dirty,
name='datasetInfos',
subname='dataset{:>04}',),
]
super(BatchIoSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, projectFileGroupName, extraSerialSlots=[]):
self.topLevelOperator = operator
SerialRoiSlot = partial(SerialListSlot,
store_transform=lambda x: -1
if x is None else x,
transform=lambda x: None if x == -1 else x,
iterable=tuple)
slots = [
SerialRoiSlot(operator.RegionStart),
SerialRoiSlot(operator.RegionStop),
SerialSlot(operator.InputMin),
SerialSlot(operator.InputMax),
SerialSlot(operator.ExportMin),
SerialSlot(operator.ExportMax),
SerialDtypeSlot(operator.ExportDtype),
SerialSlot(operator.OutputAxisOrder),
SerialSlot(operator.OutputFilenameFormat),
SerialSlot(operator.OutputInternalPath),
SerialSlot(operator.OutputFormat),
]
slots += extraSerialSlots
super(DataExportSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, projectFileGroupName):
self.predictionSlot = SerialPredictionSlot(
operator.PredictionProbabilities,
operator,
name='Predictions',
subname='predictions{:04d}',
)
slots = [
SerialListSlot(
operator.LabelNames,
),
SerialListSlot(
operator.LabelColors,
transform=lambda x: tuple(x.flat),
),
SerialListSlot(
operator.PmapColors,
transform=lambda x: tuple(x.flat),
),
SerialBlockSlot(
operator.LabelImages,
operator.LabelInputs,
operator.NonzeroLabelBlocks,
name='LabelSets',
subname='labels{:0}',
selfdepends=False,
),
self.predictionSlot,
SerialBoxSlot(
operator.opTrain.BoxConstraintRois,
operator.opTrain,
name='Rois',
subname='rois{:04d}',
),
SerialBoxSlot(
operator.opTrain.BoxConstraintValues,
operator.opTrain,
name='Values',
subname='values{:04d}',
),
SerialSlot(
operator.opTrain.Sigma,
name='Sigma',
),
SerialBoxSlot(
operator.boxViewer.rois,
operator.boxViewer,
name='ViewRois',
subname='viewrois{:04d}',
),
SerialCountingSlot(
operator.Classifier,
operator.classifier_cache,
name='CountingWrappers',
),
]
super(CountingSerializer, self).__init__(projectFileGroupName, slots=slots)
self.predictionSlot.progressSignal.subscribe(self.progressSignal)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialListSlot(operator.ChannelSelections),
SerialSlot(operator.Threshold),
SerialSlot(operator.MinSize),
SerialSlot(operator.Sigma),
SerialSlot(operator.Alpha),
SerialSlot(operator.PixelPitch),
SerialBlockSlot(
operator.Superpixels,
operator.SuperpixelCacheInput,
operator.CleanBlocks,
name="Superpixels",
subname="superpixels{:03d}",
selfdepends=False,
shrink_to_bb=False,
compression_level=1,
),
]
super(WsdtSerializer, self).__init__(projectFileGroupName, slots=slots, operator=operator)
def __init__(self, topLevelOperator, projectFileGroupName):
slots = [
SerialDictSlot(topLevelOperator.Parameters, selfdepends=True),
SerialHdf5BlockSlot(topLevelOperator.OutputHdf5,
topLevelOperator.InputHdf5,
topLevelOperator.CleanBlocks,
name="CachedOutput"),
SerialDictSlot(topLevelOperator.EventsVector,
transform=str,
selfdepends=True),
SerialDictSlot(topLevelOperator.FilteredLabels,
transform=str,
selfdepends=True),
SerialSlot(topLevelOperator.DivisionWeight),
SerialSlot(topLevelOperator.DetectionWeight),
SerialSlot(topLevelOperator.TransitionWeight),
SerialSlot(topLevelOperator.AppearanceWeight),
SerialSlot(topLevelOperator.DisappearanceWeight),
SerialSlot(topLevelOperator.MaxNumObjOut)
]
if 'MergerOutput' in topLevelOperator.outputs:
slots.append(
SerialHdf5BlockSlot(topLevelOperator.MergerOutputHdf5,
topLevelOperator.MergerInputHdf5,
topLevelOperator.MergerCleanBlocks,
name="MergerCachedOutput"), )
if 'CoordinateMap' in topLevelOperator.outputs:
slots.append(
SerialPickleableSlot(topLevelOperator.CoordinateMap, 1,
pgmlink.TimestepIdCoordinateMap()))
super(StructuredTrackingSerializer,
self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialDictSlot(operator.Crops),
SerialSlot(operator.MinValueT, selfdepends=True),
SerialSlot(operator.MaxValueT, selfdepends=True),
SerialSlot(operator.MinValueX, selfdepends=True),
SerialSlot(operator.MaxValueX, selfdepends=True),
SerialSlot(operator.MinValueY, selfdepends=True),
SerialSlot(operator.MaxValueY, selfdepends=True),
SerialSlot(operator.MinValueZ, selfdepends=True),
SerialSlot(operator.MaxValueZ, selfdepends=True),
]
super(CropSelectionSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialDictSlot(operator.FeatureNames),
SerialEdgeLabelsDictSlot(operator.EdgeLabelsDict),
SerialRagSlot(operator.Rag, operator.opRagCache,
operator.Superpixels),
SerialCachedDataFrameSlot(operator.opEdgeFeaturesCache.Output,
operator.opEdgeFeaturesCache,
name="EdgeFeatures"),
SerialClassifierSlot(operator.opClassifierCache.Output,
operator.opClassifierCache),
SerialSlot(operator.TrainRandomForest),
]
super().__init__(projectFileGroupName, slots=slots)
def __init__(self, topLevelOperator, projectFileGroupName):
slots = [
SerialSlot(topLevelOperator.PatchWidth, selfdepends=True),
SerialSlot(topLevelOperator.PatchHeight, selfdepends=True),
SerialSlot(topLevelOperator.PatchOverlapVertical,
selfdepends=True),
SerialSlot(topLevelOperator.PatchOverlapHorizontal,
selfdepends=True),
SerialSlot(topLevelOperator.GridStartVertical, selfdepends=True),
SerialSlot(topLevelOperator.GridStartHorizontal, selfdepends=True),
SerialSlot(topLevelOperator.GridWidth, selfdepends=True),
SerialSlot(topLevelOperator.GridHeight, selfdepends=True)
]
super(PatchCreatorSerializer, self).__init__(projectFileGroupName,
slots=slots)
self.topLevelOperator = topLevelOperator
def __init__(self, operator, projectFileGroupName):
slots = [
SerialSlot(operator.CurOperator, selfdepends=True),
SerialSlot(operator.MinSize, selfdepends=True),
SerialSlot(operator.MaxSize, selfdepends=True),
SerialSlot(operator.HighThreshold, selfdepends=True),
SerialSlot(operator.LowThreshold, selfdepends=True),
SerialSlot(operator.SingleThreshold, selfdepends=True),
SerialDictSlot(operator.SmootherSigma, selfdepends=True),
SerialSlot(operator.Channel, selfdepends=True),
SerialHdf5BlockSlot(operator.OutputHdf5,
operator.InputHdf5,
operator.CleanBlocks,
name="CachedThresholdOutput")
]
super(self.__class__, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, workflow, title, opCounting, isBatch=False):
# Our operator is a subclass of the generic data export operator
self._topLevelOperator = OpMultiLaneWrapper(
OpCountingDataExport,
parent=workflow,
promotedSlotNames=set(['RawData', 'Inputs', 'RawDatasetInfo']))
self._gui = None
self._title = title
self._serializers = [
DataExportSerializer(
self._topLevelOperator, title,
[SerialSlot(self._topLevelOperator.CsvFilepath)])
]
self.opCounting = opCounting
# Base class init
super(CountingDataExportApplet, self).__init__(workflow, title,
isBatch)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialSlot(operator.ChannelSelection),
SerialSlot(operator.Pmin),
SerialSlot(operator.MinMembraneSize),
SerialSlot(operator.MinSegmentSize),
SerialSlot(operator.SigmaMinima),
SerialSlot(operator.SigmaWeights),
SerialSlot(operator.GroupSeeds),
SerialSlot(operator.PreserveMembranePmaps),
SerialBlockSlot(operator.Superpixels,
operator.SuperpixelCacheInput,
operator.CleanBlocks,
name='Superpixels',
subname='superpixels{:03d}',
selfdepends=False,
shrink_to_bb=False,
compression_level=1)
]
super(WsdtSerializer, self).__init__(projectFileGroupName,
slots=slots,
operator=operator)
def __init__(self, topGroupName, operator):
serialSlots = [
SerialDictSlot(operator.SelectedFeatures, transform=str),
SerialListSlot(operator.LabelNames, transform=str),
SerialListSlot(operator.LabelColors,
transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors,
transform=lambda x: tuple(x.flat)),
SerialDictSlot(operator.LabelInputs, transform=int),
SerialClassifierSlot(operator.Classifier,
operator.classifier_cache,
name="ClassifierForests"),
SerialDictSlot(operator.CachedProbabilities,
operator.InputProbabilities,
transform=int),
SerialSlot(operator.MaxNumObj),
SerialPickledValueSlot(operator.ExportSettings)
]
super(ObjectClassificationSerializer, self).__init__(topGroupName,
slots=serialSlots,
operator=operator)
def __init__(self, topGroupName, operator):
self.VERSION = 1 # Make sure to bump the version in case you make any changes in the serialization
serialSlots = [
SerialDictSlot(operator.SelectedFeatures),
SerialListSlot(operator.LabelNames),
SerialListSlot(operator.LabelColors,
transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors,
transform=lambda x: tuple(x.flat)),
SerialDictSlot(operator.LabelInputs, transform=int),
SerialClassifierSlot(operator.Classifier,
operator.classifier_cache,
name="ClassifierForests"),
SerialDictSlot(operator.CachedProbabilities,
operator.InputProbabilities,
transform=int),
SerialSlot(operator.MaxNumObj),
SerialPickleableSlot(operator.ExportSettings, self.VERSION, None)
]
super(ObjectClassificationSerializer, self).__init__(topGroupName,
slots=serialSlots,
operator=operator)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialSlot(operator.CurOperator, selfdepends=True),
SerialSlot(operator.MinSize, selfdepends=True),
SerialSlot(operator.MaxSize, selfdepends=True),
SerialSlot(operator.HighThreshold, selfdepends=True),
SerialSlot(operator.LowThreshold, selfdepends=True),
SerialDictSlot(operator.SmootherSigma, selfdepends=True),
SerialSlot(operator.Channel, selfdepends=True),
SerialSlot(operator.CoreChannel, selfdepends=True),
SerialBlockSlot(operator.CachedOutput,
operator.CacheInput,
operator.CleanBlocks,
name='CachedThresholdLabels',
subname='threshold{:03d}',
selfdepends=False,
shrink_to_bb=False,
compression_level=1)
]
super(self.__class__, self).__init__(projectFileGroupName, slots,
operator)
def __init__(self, operator, projectFileGroupName):
super(NanshePostprocessingSerializer, self).__init__(projectFileGroupName,
slots=[SerialSlot(operator.SignificanceThreshold, selfdepends=True),
SerialListSlot(operator.WaveletTransformScale, selfdepends=True),
SerialSlot(operator.NoiseThreshold, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Min, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Max, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled, selfdepends=True),
SerialSlot(operator.MinLocalMaxDistance, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Min, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Min_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Max, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Max_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Min, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Max, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled, selfdepends=True),
SerialSlot(operator.AlignmentMinThreshold, selfdepends=True),
SerialSlot(operator.OverlapMinThreshold, selfdepends=True),
SerialSlot(operator.Fuse_FractionMeanNeuronMaxThreshold, selfdepends=True),
SerialBlockSlot(operator.Output,
operator.CacheInput,
operator.CleanBlocks, selfdepends=True)])
def __init__(self, topLevelOperator, projectFileGroupName):
self.VERSION = 1 # Make sure to bump the version in case you make any changes in the serialization
try:
slots = [
SerialDictSlot(topLevelOperator.Parameters, selfdepends=True),
SerialDictSlot(topLevelOperator.FilteredLabels,
transform=str,
selfdepends=True),
SerialPickleableSlot(topLevelOperator.ExportSettings,
version=self.VERSION),
SerialPickleableSlot(topLevelOperator.HypothesesGraph,
version=self.VERSION),
SerialPickleableSlot(topLevelOperator.LearningHypothesesGraph,
version=self.VERSION),
SerialPickleableSlot(topLevelOperator.ResolvedMergers,
version=self.VERSION),
SerialSlot(topLevelOperator.DivisionWeight),
SerialSlot(topLevelOperator.DetectionWeight),
SerialSlot(topLevelOperator.TransitionWeight),
SerialSlot(topLevelOperator.AppearanceWeight),
SerialSlot(topLevelOperator.DisappearanceWeight),
SerialSlot(topLevelOperator.MaxNumObjOut),
]
except:
slots = [
SerialDictSlot(topLevelOperator.Parameters, selfdepends=True),
SerialDictSlot(topLevelOperator.FilteredLabels,
transform=str,
selfdepends=True),
SerialPickleableSlot(topLevelOperator.ExportSettings,
version=self.VERSION),
SerialPickleableSlot(topLevelOperator.ResolvedMergers,
version=self.VERSION),
SerialSlot(topLevelOperator.DivisionWeight),
SerialSlot(topLevelOperator.DetectionWeight),
SerialSlot(topLevelOperator.TransitionWeight),
SerialSlot(topLevelOperator.AppearanceWeight),
SerialSlot(topLevelOperator.DisappearanceWeight),
SerialSlot(topLevelOperator.MaxNumObjOut),
]
super(StructuredTrackingSerializer,
self).__init__(projectFileGroupName,
slots=slots,
operator=topLevelOperator)
def __init__(self, projectFileGroupName, operator):
super().__init__(projectFileGroupName, [SerialSlot(operator.ServerId)])
def __init__(self, topLevelOperator, projectFileGroupName):
if WITH_HYTRA:
slots = [ SerialDictSlot(topLevelOperator.Parameters, selfdepends=True),
SerialDictSlot(topLevelOperator.EventsVector, transform=str, selfdepends=True),
SerialDictSlot(topLevelOperator.FilteredLabels, transform=str, selfdepends=True),
SerialPickledValueSlot(topLevelOperator.ExportSettings),
SerialPickledValueSlot(topLevelOperator.HypothesesGraph),
SerialPickledValueSlot(topLevelOperator.ResolvedMergers),
SerialSlot(topLevelOperator.DivisionWeight),
SerialSlot(topLevelOperator.DetectionWeight),
SerialSlot(topLevelOperator.TransitionWeight),
SerialSlot(topLevelOperator.AppearanceWeight),
SerialSlot(topLevelOperator.DisappearanceWeight),
SerialSlot(topLevelOperator.MaxNumObjOut)
]
else:
try:
import pgmlink
except:
import pgmlinkNoIlpSolver as pgmlink
slots = [ SerialDictSlot(topLevelOperator.Parameters, selfdepends=True),
SerialHdf5BlockSlot(topLevelOperator.OutputHdf5,
topLevelOperator.InputHdf5,
topLevelOperator.CleanBlocks,
name="CachedOutput"),
SerialDictSlot(topLevelOperator.EventsVector, transform=str, selfdepends=True),
SerialDictSlot(topLevelOperator.FilteredLabels, transform=str, selfdepends=True),
SerialSlot(topLevelOperator.DivisionWeight),
SerialSlot(topLevelOperator.DetectionWeight),
SerialSlot(topLevelOperator.TransitionWeight),
SerialSlot(topLevelOperator.AppearanceWeight),
SerialSlot(topLevelOperator.DisappearanceWeight),
SerialSlot(topLevelOperator.MaxNumObjOut)
]
if 'MergerOutput' in topLevelOperator.outputs:
slots.append(SerialHdf5BlockSlot(topLevelOperator.MergerOutputHdf5,
topLevelOperator.MergerInputHdf5,
topLevelOperator.MergerCleanBlocks,
name="MergerCachedOutput"),
)
if 'CoordinateMap' in topLevelOperator.outputs:
slots.append(SerialPickleableSlot(topLevelOperator.CoordinateMap, 1, pgmlink.TimestepIdCoordinateMap()))
super(StructuredTrackingSerializer, self ).__init__(projectFileGroupName, slots=slots, operator=topLevelOperator)
def __init__(self, operator: "OpCarving", groupName):
super().__init__(groupName, slots=[SerialSlot(operator.ObjectPrefix)])
self._o = operator
def __init__(self, operator, projectFileGroupName):
super(NansheDictionaryLearningSerializer,
self).__init__(projectFileGroupName,
slots=[
SerialSlot(operator.Ord, selfdepends=True),
SerialSlot(operator.K, selfdepends=True),
SerialSlot(operator.Gamma1, selfdepends=True),
SerialSlot(operator.Gamma2, selfdepends=True),
SerialSlot(operator.NumThreads,
selfdepends=True),
SerialSlot(operator.Batchsize,
selfdepends=True),
SerialSlot(operator.NumIter,
selfdepends=True),
SerialSlot(operator.Lambda1,
selfdepends=True),
SerialSlot(operator.Lambda2,
selfdepends=True),
SerialSlot(operator.PosAlpha,
selfdepends=True),
SerialSlot(operator.PosD, selfdepends=True),
SerialSlot(operator.Clean, selfdepends=True),
SerialSlot(operator.Mode, selfdepends=True),
SerialSlot(operator.ModeD, selfdepends=True),
SerialBlockSlot(operator.Output,
operator.CacheInput,
operator.CleanBlocks,
selfdepends=True)
])
def __init__(self, operator, projectFileGroupName):
super(NanshePreprocessingSerializer, self).__init__(
projectFileGroupName,
slots=[
SerialSlot(operator.ToRemoveZeroedLines, selfdepends=True),
SerialListSlot(operator.ErosionShape, selfdepends=True),
SerialListSlot(operator.DilationShape, selfdepends=True),
SerialSlot(operator.ToExtractF0, selfdepends=True),
SerialSlot(operator.HalfWindowSize, selfdepends=True),
SerialSlot(operator.WhichQuantile, selfdepends=True),
SerialSlot(operator.TemporalSmoothingGaussianFilterStdev,
selfdepends=True),
SerialSlot(operator.SpatialSmoothingGaussianFilterStdev,
selfdepends=True),
SerialSlot(operator.TemporalSmoothingGaussianFilterWindowSize,
selfdepends=True),
SerialSlot(operator.SpatialSmoothingGaussianFilterWindowSize,
selfdepends=True),
SerialSlot(operator.BiasEnabled, selfdepends=True),
SerialSlot(operator.Bias, selfdepends=True),
SerialSlot(operator.ToWaveletTransform, selfdepends=True),
SerialListSlot(operator.Scale, selfdepends=True),
SerialBlockSlot(operator.CacheOutput,
operator.CacheInput,
operator.CleanBlocks,
selfdepends=True)
])