def trainingHookFDR(net, path, report):
"""
This training hook is used extensively by the FDR experiments. It prints
out the learned information about each level in the network
"""
# Print header
print "\n## Executing trainingHook...."
# Print info for each level
tierCount = VU.getTierCount(net)
# This loop is temporary until NuPIC 2 allows retrieval of Region names and
# node types
regions = net.regions
for name, r in regions.items():
if 'FDR' in r.type or 'CLARegion' in r.type:
if not r.getParameter('disableTemporal'):
msg = "\n# Temporal node info for %s (nodetype %s):" % (name,
r.type)
print msg
report.write(msg + os.linesep)
for paramName in [
'tpNumCells', 'tpNumSegments', 'tpNumSynapses',
'tpNumSynapsesPerSegmentMax',
'tpNumSynapsesPerSegmentAvg'
]:
msg = "# %s: %s" % (paramName,
str(r.getParameter(paramName)))
print msg
report.write(msg + os.linesep)
if not r.getParameter('disableSpatial'):
msg = "\n# Spatial node info for %s (nodetype %s):" % (name,
r.type)
print msg
report.write(msg + os.linesep)
if 'spLearningStatsStr' in r.spec.parameters:
stats = eval(r.getParameter('spLearningStatsStr'))
keys = stats.keys()
keys.sort()
for key in keys:
msg = "# %-25s: %s" % (key, str(stats[key]))
print msg
report.write(msg + os.linesep)
def trainingHookFDR(net, path, report):
"""
This training hook is used extensively by the FDR experiments. It prints
out the learned information about each level in the network
"""
# Print header
print "\n## Executing trainingHook...."
# Print info for each level
tierCount = VU.getTierCount(net)
# This loop is temporary until NuPIC 2 allows retrieval of Region names and
# node types
regions = net.regions
for name, r in regions.items():
if 'FDR' in r.type or 'CLARegion' in r.type:
if not r.getParameter('disableTemporal'):
msg = "\n# Temporal node info for %s (nodetype %s):" % (name, r.type)
print msg
report.write(msg + os.linesep)
for paramName in ['tpNumCells', 'tpNumSegments', 'tpNumSynapses',
'tpNumSynapsesPerSegmentMax',
'tpNumSynapsesPerSegmentAvg']:
msg = "# %s: %s" % (paramName, str(r.getParameter(paramName)))
print msg
report.write(msg + os.linesep)
if not r.getParameter('disableSpatial'):
msg = "\n# Spatial node info for %s (nodetype %s):" % (name, r.type)
print msg
report.write(msg + os.linesep)
if 'spLearningStatsStr' in r.spec.parameters:
stats = eval(r.getParameter('spLearningStatsStr'))
keys = stats.keys()
keys.sort()
for key in keys:
msg = "# %-25s: %s" % (key, str(stats[key]))
print msg
report.write(msg + os.linesep)
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
# If no background image, just return the input image as-is
if self.bgPath is None:
return image
# ---------------------------------------------------------------------------
# Open the background image(s) if we haven't done so already
if self.bgImgs is None:
# If given a relative path, make it relative to the vision data directory
if not os.path.isabs(self.bgPath):
basePath = os.path.abspath(os.curdir)
basePath = os.path.split(basePath)
while(basePath[0]):
if basePath[1] == 'vision':
break
basePath = os.path.split(basePath[0])
# Did we find the vision directory?
if basePath[1] == 'vision':
fullPath = VisionUtils.findData(os.path.join(basePath[0], 'vision'),
self.bgPath, 'backgound', 'background images', True)
#basePath = os.path.join(basePath[0], 'vision', 'data')
else:
fullPath = self.bgPath
else:
fullPath = self.bgPath
# If given a filename, we only have 1 image
if os.path.isfile(fullPath):
self.bgImgs = [Image.open(fullPath).convert('LA')]
# Else, open up all images in this directory
else:
self.bgImgs = []
w = os.walk(fullPath)
while True:
try:
dirpath, dirnames, filenames = w.next()
except StopIteration:
break
# Don't enter directories that begin with '.'
for d in dirnames[:]:
if d.startswith('.'):
dirnames.remove(d)
dirnames.sort()
# Ignore files that begin with '.'
filenames = [f for f in filenames if not f.startswith('.')]
filenames.sort()
imageFilenames = [os.path.join(dirpath, f) for f in filenames]
# Process each image
for filename in imageFilenames:
self.bgImgs.append(Image.open(filename).convert('L'))
# Keep a cache of all images, scaled to the input image size
self.scaledBGImgs = [x.copy() for x in self.bgImgs]
# Pick a background at random.
idx = self._rng.randint(0, len(self.bgImgs)-1)
bgImg = self.scaledBGImgs[idx]
# ---------------------------------------------------------------------------
# re-scale the background to the source image if necessary
if bgImg.size != image.size:
bgImg = self.scaledBGImgs[idx] = self.bgImgs[idx].resize(image.size, Image.ANTIALIAS)
# ---------------------------------------------------------------------------
# Create the mask around the source image
mask = image.split()[-1]
if image.mode[-1] != 'A' or isSimpleBBox(mask):
mask = createMask(image, threshold=self._threshold, fillHoles=True,
backgroundColor=self.background, blurRadius=self._blurRadius,
maskScale=self._maskScale)
# ---------------------------------------------------------------------------
# Paste the image onto the background
newImage = bgImg.copy()
newImage.paste(image, (0,0), mask)
# Put an "all-on" alpha channel because we now want the network to consider the entire
# image
newImage.putalpha(ImageChops.constant(newImage, 255))
return newImage
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 process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
# If no background image, just return the input image as-is
if self.bgPath is None:
return image
# ---------------------------------------------------------------------------
# Open the background image(s) if we haven't done so already
if self.bgImgs is None:
# If given a relative path, make it relative to the vision data directory
if not os.path.isabs(self.bgPath):
basePath = os.path.abspath(os.curdir)
basePath = os.path.split(basePath)
while (basePath[0]):
if basePath[1] == 'vision':
break
basePath = os.path.split(basePath[0])
# Did we find the vision directory?
if basePath[1] == 'vision':
fullPath = VisionUtils.findData(
os.path.join(basePath[0], 'vision'), self.bgPath,
'backgound', 'background images', True)
#basePath = os.path.join(basePath[0], 'vision', 'data')
else:
fullPath = self.bgPath
else:
fullPath = self.bgPath
# If given a filename, we only have 1 image
if os.path.isfile(fullPath):
self.bgImgs = [Image.open(fullPath).convert('LA')]
# Else, open up all images in this directory
else:
self.bgImgs = []
w = os.walk(fullPath)
while True:
try:
dirpath, dirnames, filenames = w.next()
except StopIteration:
break
# Don't enter directories that begin with '.'
for d in dirnames[:]:
if d.startswith('.'):
dirnames.remove(d)
dirnames.sort()
# Ignore files that begin with '.'
filenames = [f for f in filenames if not f.startswith('.')]
filenames.sort()
imageFilenames = [
os.path.join(dirpath, f) for f in filenames
]
# Process each image
for filename in imageFilenames:
self.bgImgs.append(Image.open(filename).convert('L'))
# Keep a cache of all images, scaled to the input image size
self.scaledBGImgs = [x.copy() for x in self.bgImgs]
# Pick a background at random.
idx = self._rng.randint(0, len(self.bgImgs) - 1)
bgImg = self.scaledBGImgs[idx]
# ---------------------------------------------------------------------------
# re-scale the background to the source image if necessary
if bgImg.size != image.size:
bgImg = self.scaledBGImgs[idx] = self.bgImgs[idx].resize(
image.size, Image.ANTIALIAS)
# ---------------------------------------------------------------------------
# Create the mask around the source image
mask = image.split()[-1]
if image.mode[-1] != 'A' or isSimpleBBox(mask):
mask = createMask(image,
threshold=self._threshold,
fillHoles=True,
backgroundColor=self.background,
blurRadius=self._blurRadius,
maskScale=self._maskScale)
# ---------------------------------------------------------------------------
# Paste the image onto the background
newImage = bgImg.copy()
newImage.paste(image, (0, 0), mask)
# Put an "all-on" alpha channel because we now want the network to consider the entire
# image
newImage.putalpha(ImageChops.constant(newImage, 255))
return newImage
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()