def __init__(self, parent, network, numTopResults=5):
NetworkInspector.__init__(self, parent, network)
self.classifier = None
# Find the classifier
for element in _getElements(network):
if 'categoriesOut' in element.spec.outputs:
self.classifier = element
break
else:
import warnings
warnings.warn("No classifier found (no region with 'categoriesOut')",
stacklevel=1)
# Get the categoryInfo from the sensor
sensor = _getElement(network, 'sensor')
self.categoryInfo = _getSelf(sensor).getParameter('categoryInfo') \
if _hasParameter(sensor, 'categoryInfo') else []
# Truncate numTopResults based on number of categories
numTopResults = min(numTopResults, len(self.categoryInfo))
numTopResults = max(3, numTopResults) # Absolute minimum
self.numTopResults = numTopResults
# Create the view
self.traits_view = View(
Item('inferenceResults', show_label=False,
editor=InferenceResultsEditor(numTopResults=self.numTopResults))
)
def __init__(self, parent, network, numTopResults=5):
NetworkInspector.__init__(self, parent, network)
self.classifier = None
# Find the classifier
for element in _getElements(network):
if 'categoriesOut' in element.spec.outputs:
self.classifier = element
break
else:
import warnings
warnings.warn(
"No classifier found (no region with 'categoriesOut')",
stacklevel=1)
# Get the categoryInfo from the sensor
sensor = _getElement(network, 'sensor')
self.categoryInfo = _getSelf(sensor).getParameter('categoryInfo') \
if _hasParameter(sensor, 'categoryInfo') else []
# Truncate numTopResults based on number of categories
numTopResults = min(numTopResults, len(self.categoryInfo))
numTopResults = max(3, numTopResults) # Absolute minimum
self.numTopResults = numTopResults
# Create the view
self.traits_view = View(
Item('inferenceResults',
show_label=False,
editor=InferenceResultsEditor(
numTopResults=self.numTopResults)))
def __init__(self, parent, experiment, tierCount=0, showProgressBar=False,
multipleDatasets=False, startImmediately=True):
"""
"""
#title()
self.experiment = experiment
self.pause = True
self.experiment.pause = True
self.runCount = None
network = experiment.network
NetworkInspector.__init__(self, parent, network)
self.tierCount = tierCount
self.showProgressBar = showProgressBar
self.multipleDatasets = multipleDatasets
self.stopTarget = None
self.iteration = 0
self.iterationCount = 0
self.usingPauseTarget = False
self.stepping = False
# Look for all sensors (regions without inputs)
# If all sensors are of a supported type, seeking backward is supported
# Otherwise, seeking backward is not supported, because not all sensors
# can be run in reverse
self.sensors = []
for element in _getElements(self.network):
if _isSensor(element):
if element.type == 'VectorFileSensor':
self.sensors.append(element)
else:
# Unsupported sensor type
self.sensors = []
break
# Set the stop button label differently for training and testing
# The stop button isn't shown at all if neither training nor testing, so
# don't worry about that case
if not self.tierCount and self.multipleDatasets:
self.add_trait('stopButton', Button(label='Skip to next dataset'))
else:
self.add_trait('stopButton', Button(label='Stop training this tier'))
# Set up the default pause target (next error), which the user will be
# able to change later
#self._createDefaultPauseTarget()
#self.pauseTarget = self.defaultPauseTarget
# Set up the Traits view
self._createView()
if startImmediately:
self.start() # Start running the network
def __init__(self, parent, network):
NetworkInspector.__init__(self, parent, network)
# Find the classifier
self.classifier = _findClassifier(network)
if not self.classifier:
raise RuntimeError("No classifier found (no region with 'categoriesOut')")
# Get the categoryInfo from the sensor
sensor = _getElement(network, 'sensor')
self.categoryInfo = _getCategoryInfo(sensor)
self.catNames = [cat[0] for cat in self.categoryInfo]
# Acquire reference to the gabor region
if 'GaborRegion' in _getRegionType(network, 'level1SP'):
self._hasGabor = True
self._gaborRegion = None
self._gaborProxy = None
gaborRegion = _getElement(network, VisionUtils.getTierName(1, network=network))
if gaborRegion:
self._gaborProxy = _getGaborProxy(gaborRegion)
# Obtain gabor scales
gaborInputDims = self._gaborProxy._inputDims
baseWidth = float(gaborInputDims[0][1])
self.scales = [float(dims[1])/baseWidth for dims in gaborInputDims]
# Used to store cached gabor responses
self._cachedResponses = {}
else:
self._gaborRegion = None
self._hasGabor = False
self.showTitle = False
self.mode = 'unfiltered'
self.filterOnCategory = False
self.logDir = ""
self._bboxes = None
self._imageFilenames = None
# Check if our classifier complies with the required API:
self._compliantClassifier = _classifierComplies(self.classifier)
if self._compliantClassifier:
# Convert category labels to numpy
c = _getSelf(self.classifier)
categoryList = c.getCategoryList()
if categoryList is None:
categoryList = []
self._catLabels = numpy.array(categoryList)
self.numPrototypes = "Total Prototypes: %d" % len(self._catLabels)
# Parameters that need to be handled with custom code in updateParameters
customParameters = {
'numPresentationsInLog': Str,
'logDir': Str,
# Used for displaying "Distance:"
'dummy': Str
}
for name, trait in customParameters.iteritems():
self.add_trait(name, trait(label=name))
self._createDefaultImages()
# Add traits
if not self._hasGabor:
availableSpaces = ['pixel']
else:
availableSpaces = ['pixel', 'gabor']
for k in xrange(self._numNeighbors):
for spaceType in availableSpaces:
traitName = '%s%03d' % (spaceType, k)
self.add_trait(traitName, Instance(PIL.Image.Image))
exec "self.%s = self._missingImage" % traitName
traitName = 'score%03d' % k
self.add_trait(traitName, Str)
protoScoreStr = self._makeScoreString()
exec "self.%s = protoScoreStr" % traitName
traitName = 'imagename%03d' % k
self.add_trait(traitName, Str)
imagename = ""
exec "self.%s = imagename" % traitName
self.createView()
def __init__(self, parent, network):
NetworkInspector.__init__(self, parent, network)
# Find the classifier
self.classifier = _findClassifier(network)
if not self.classifier:
raise RuntimeError(
"No classifier found (no region with 'categoriesOut')")
# Get the categoryInfo from the sensor
sensor = _getElement(network, 'sensor')
self.categoryInfo = _getCategoryInfo(sensor)
self.catNames = [cat[0] for cat in self.categoryInfo]
# Acquire reference to the gabor region
if 'GaborRegion' in _getRegionType(network, 'level1SP'):
self._hasGabor = True
self._gaborRegion = None
self._gaborProxy = None
gaborRegion = _getElement(
network, VisionUtils.getTierName(1, network=network))
if gaborRegion:
self._gaborProxy = _getGaborProxy(gaborRegion)
# Obtain gabor scales
gaborInputDims = self._gaborProxy._inputDims
baseWidth = float(gaborInputDims[0][1])
self.scales = [
float(dims[1]) / baseWidth for dims in gaborInputDims
]
# Used to store cached gabor responses
self._cachedResponses = {}
else:
self._gaborRegion = None
self._hasGabor = False
self.showTitle = False
self.mode = 'unfiltered'
self.filterOnCategory = False
self.logDir = ""
self._bboxes = None
self._imageFilenames = None
# Check if our classifier complies with the required API:
self._compliantClassifier = _classifierComplies(self.classifier)
if self._compliantClassifier:
# Convert category labels to numpy
c = _getSelf(self.classifier)
categoryList = c.getCategoryList()
if categoryList is None:
categoryList = []
self._catLabels = numpy.array(categoryList)
self.numPrototypes = "Total Prototypes: %d" % len(self._catLabels)
# Parameters that need to be handled with custom code in updateParameters
customParameters = {
'numPresentationsInLog': Str,
'logDir': Str,
# Used for displaying "Distance:"
'dummy': Str
}
for name, trait in customParameters.iteritems():
self.add_trait(name, trait(label=name))
self._createDefaultImages()
# Add traits
if not self._hasGabor:
availableSpaces = ['pixel']
else:
availableSpaces = ['pixel', 'gabor']
for k in xrange(self._numNeighbors):
for spaceType in availableSpaces:
traitName = '%s%03d' % (spaceType, k)
self.add_trait(traitName, Instance(PIL.Image.Image))
exec "self.%s = self._missingImage" % traitName
traitName = 'score%03d' % k
self.add_trait(traitName, Str)
protoScoreStr = self._makeScoreString()
exec "self.%s = protoScoreStr" % traitName
traitName = 'imagename%03d' % k
self.add_trait(traitName, Str)
imagename = ""
exec "self.%s = imagename" % traitName
self.createView()
def __init__(self,
parent,
experiment,
tierCount=0,
showProgressBar=False,
multipleDatasets=False,
startImmediately=True):
"""
"""
#title()
self.experiment = experiment
self.pause = True
self.experiment.pause = True
self.runCount = None
network = experiment.network
NetworkInspector.__init__(self, parent, network)
self.tierCount = tierCount
self.showProgressBar = showProgressBar
self.multipleDatasets = multipleDatasets
self.stopTarget = None
self.iteration = 0
self.iterationCount = 0
self.usingPauseTarget = False
self.stepping = False
# Look for all sensors (regions without inputs)
# If all sensors are of a supported type, seeking backward is supported
# Otherwise, seeking backward is not supported, because not all sensors
# can be run in reverse
self.sensors = []
for element in _getElements(self.network):
if _isSensor(element):
if element.type == 'VectorFileSensor':
self.sensors.append(element)
else:
# Unsupported sensor type
self.sensors = []
break
# Set the stop button label differently for training and testing
# The stop button isn't shown at all if neither training nor testing, so
# don't worry about that case
if not self.tierCount and self.multipleDatasets:
self.add_trait('stopButton', Button(label='Skip to next dataset'))
else:
self.add_trait('stopButton',
Button(label='Stop training this tier'))
# Set up the default pause target (next error), which the user will be
# able to change later
#self._createDefaultPauseTarget()
#self.pauseTarget = self.defaultPauseTarget
# Set up the Traits view
self._createView()
if startImmediately:
self.start() # Start running the network
