def computeLayerData(self, layerOut):
"""
Override computeLayer so we call into C++ code for fast performance.
@param layerOut: the output HmaxLayer to store results in.
"""
if len(self.learned) == 0: # if untrained, do not compute
return
if self.cLearnedW == None:
self.cLearnedW = hmaxc.floatCArray(len(self.learned) * 16)
self.cLearnedPF = hmaxc.floatCArray(len(self.learned) * 16)
for f in xrange(len(self.learned)):
fi = f * 16
# self.learned is [f][4][4][2] array...need to copy correctly
for xi in xrange(4):
for yi in xrange(4):
w, pf = self.learned[f][xi][yi]
self.cLearnedW[fi + ((yi * 4) + xi)] = w
self.cLearnedPF[fi + ((yi * 4) + xi)] = pf
# print "C++ GRBF:"
layerIn = layerOut.inputLayers[0]
self.cGRBFFilter.computeLayer(
self.cLearnedW, self.cLearnedPF, len(self.learned) * 16, layerIn.cLayer, layerOut.cLayer
)
layerOut.setLayerDataFromCArray(checkSame=True)
def __init__(self, level, xySize, fSize, xyStart, xySpace, inputLayers=[]):
Layer.__init__(self, level, xySize, fSize, xyStart, xySpace, inputLayers)
#int xSize, int ySize, int fSize, float xStart, float yStart,
# float xSpace, float ySpace
self.cArray = hmaxc.floatCArray(self.fSize * self.xSize*self.ySize)
self.cLayer = hmaxc.LayerC(xySize[0], xySize[1], fSize, xyStart[0], \
xyStart[1], xySpace[0], xySpace[1], self.cArray)
def __init__(self, level, xySize, fSize, xyStart, xySpace, inputLayers=[]):
Layer.__init__(self, level, xySize, fSize, xyStart, xySpace,
inputLayers)
#int xSize, int ySize, int fSize, float xStart, float yStart,
# float xSpace, float ySpace
self.cArray = hmaxc.floatCArray(self.fSize * self.xSize * self.ySize)
self.cLayer = hmaxc.LayerC(xySize[0], xySize[1], fSize, xyStart[0], \
xyStart[1], xySpace[0], xySpace[1], self.cArray)
def getLayerDataAsCArray(self, f=0):
"""
Return a copy of the layer's data matrix for index f converted
into a flattened 1D float array (C float[]).
"""
if self.cArray == None:
self.cArray = hmaxc.floatCArray(self.xSize * self.ySize)
for j in xrange(self.ySize):
for i in xrange(self.xSize):
self.cArray[(j * self.xSize) + i] = self.array[f][i][j]
return self.cArray
def getLayerDataAsCArray(self, f=0):
"""
Return a copy of the layer's data matrix for index f converted
into a flattened 1D float array (C float[]).
"""
if self.cArray == None:
self.cArray = hmaxc.floatCArray(self.xSize * self.ySize)
for j in xrange(self.ySize):
for i in xrange(self.xSize):
self.cArray[(j * self.xSize) + i] = self.array[f][i][j]
return self.cArray
def computeLayerData(self, layerOut):
"""
Override computeLayer so we call into C++ code for fast performance.
@param layerOut: the output HmaxLayer to store results in.
"""
if len(self.learned)==0: #if untrained, do not compute
return
if self.cLearnedW==None:
self.cLearnedW = hmaxc.floatCArray(len(self.learned)*16)
self.cLearnedPF = hmaxc.floatCArray(len(self.learned)*16)
for f in xrange(len(self.learned)):
fi = f*16
#self.learned is [f][4][4][2] array...need to copy correctly
for xi in xrange(4):
for yi in xrange(4):
w,pf = self.learned[f][xi][yi]
self.cLearnedW[fi+((yi*4)+xi)] = w
self.cLearnedPF[fi+((yi*4)+xi)] = pf
#print "C++ GRBF:"
layerIn = layerOut.inputLayers[0]
self.cGRBFFilter.computeLayer(self.cLearnedW, self.cLearnedPF, \
len(self.learned)*16, layerIn.cLayer, layerOut.cLayer)
layerOut.setLayerDataFromCArray(checkSame=True)
def __init__(self, thetas, size=11, lam=5.6, sigma=4.5, aspect=0.3):
"""
@param size: the integer size (length and width) for this filter.
@param thetas: the set of angle orientations, in radians, to use for this filter.
@param lam: the lambda wavelength this filter is tuned with.
@param sigma:
@param aspect: the aspect ratio.
"""
#GaborFilterC(float thetas[], int thetaCount, int size=11,
# float lam=5.6, float sigma=4.5, float aspect=0.3);
self.size = size
cthetas = hmaxc.floatCArray(len(thetas))
for i in xrange(len(thetas)):
print "pyTheta"+str(i)+": ",thetas[i]
cthetas[i] = thetas[i]
self.cGabor = hmaxc.GaborFilterC(cthetas, len(thetas), size, lam, sigma, aspect)
def __init__(self, thetas, size=11, lam=5.6, sigma=4.5, aspect=0.3):
"""
@param size: the integer size (length and width) for this filter.
@param thetas: the set of angle orientations, in radians, to use for this filter.
@param lam: the lambda wavelength this filter is tuned with.
@param sigma:
@param aspect: the aspect ratio.
"""
#GaborFilterC(float thetas[], int thetaCount, int size=11,
# float lam=5.6, float sigma=4.5, float aspect=0.3);
self.size = size
cthetas = hmaxc.floatCArray(len(thetas))
for i in xrange(len(thetas)):
#print "pyTheta"+str(i)+": ",thetas[i]
cthetas[i] = thetas[i]
self.cGabor = hmaxc.GaborFilterC(cthetas, len(thetas), size, lam,
sigma, aspect)
