I have written code within Python that doesn’t release memory the way it should. The memory is taken by Python but never gets released even after not being used anymore. Even if you break the running program with ctrl+c. Delete the variable and run gc.collect() it doesn’t seem to collect. Or the same as in Ipython and running %reset. The memory won’t be freed and running gc.collect() has no effect. I tested this in Windows because I wanted to see if it could possibly be with the garbage collector library. It appears that is the case. Run the code below in Linux and then also in windows. Then compare the memory usage. You will need numpy and scipy installed. Any help or insight on this issue would be much appreciated.
Import the Model, create an instance, and then run createSpecific().
Here is a code that exhibits this behavior in Ubuntu 10.04:
from numpy import array, maximum,intersect1d, meshgrid, std, log, log10, zeros, ones, argwhere, abs, arange, size, copy, sqrt, sin, cos, pi, vstack, hstack, zeros, exp, max, mean, savetxt, loadtxt, minimum, linspace, where
from numpy.fft import fft
from scipy.stats import f_oneway, kruskal, sem, scoreatpercentile
#import matplotlib
#matplotlib.use('cairo.pdf')
from matplotlib.pyplot import plot, clf, show, cla, xlim, xscale, imshow, ylabel, xlabel, figure, savefig, close, bar, title, xticks, yticks, axes, axis
from matplotlib.axes import Axes
from mpl_toolkits.mplot3d import Axes3D
#from enthought.mayavi import mlab
from matplotlib import cm
import matplotlib.pyplot as plt
import os
from time import clock
from timeit import Timer
class Model:
#Constructors and default includes
def __init__(self, prevAud = None, debug=False):
if (prevAud == None):
self.fs=16000. #sample rate
self.lowFreq=60.
self.hiFreq=5000.
self.numFilt=300 #number of channel
self.EarQ = 9.26449 #9.26449
self.minBW = 24.7 #24.7
self.integrationWindow=.01
self.sliceAt=.035
self.maxOverallInhibit = 0.1
self.winLen = int(self.fs*self.integrationWindow+.01) #default integration window 10 ms
self.fullWind = 0.300
self.outShortWindow = None
self.siderArray = None
self.maxNormalizeValue = .284 # Optimized at .284
self.outputSemiModel = None
self.semitones = 11
self.activationTrace = None
return
def setErbScale(self, erbScale = None):
if (erbScale ==None):
self.erbScale = arange(100,500,5)
else:
self.erbScale = erbScale
def trainModel(self,soundVec=None, fs=None, lowfreq=None, highfreq=None, numfilt=None, figto=0, savefig = 'N', prompts=False, plotter=False):
self.setErbScale()
templateArray = self.intWindow(self.halfWaveRec(self.creGammatone(soundVec)))
for i in xrange(templateArray[0].size):
self.outerTest(self.innerTest(templateArray[:,i]))
return templateArray
def createSpecific(self, freqArray = None, semitones = 11, timeforHarm = .3, soundVec=None, fs=None, lowfreq=None, highfreq=None, numfilt=None, figto=0, saveData='N', fileDir='TempRunT/', prompts=False, plotter=False):
if (freqArray == None):
self.setErbScale()
freqArray = self.erbScale
if (type(semitones) == int):
semitones = arange(semitones+1)
totalRuns = int(timeforHarm/self.integrationWindow+.001)
inhibitWindowArray = zeros((freqArray.size,(semitones.size),self.numFilt,totalRuns))
for x in xrange(freqArray.size):
tempHarm = self.makeHarmonicAmpMod(freqArray[x],timeforHarm, numHarm=7,modulation=10)
for y in semitones:
tempChord = self.makeSemiChordAmpMod(tempHarm, freqArray[x],timeforHarm,modulation=10,numHarm=7,semi=y)
inhibitWindowArray[x,y] = self.trainModel( tempChord, savefig = 'N', plotter=plotter)
self.inhibitWindowArray = inhibitWindowArray
def creGammatone(self, soundVec):
temp = zeros((300,soundVec.size))
for i in xrange(temp[:,0].size):
temp[i] = -1**i*soundVec
return temp
def halfWaveRec(self, halfWaveFilts):
filtShape = halfWaveFilts.shape
if (filtShape[1] != int(self.fs*self.fullWind)):
halfWaveFilts = hstack((halfWaveFilts,zeros((self.numFilt,int(self.fs*self.fullWind)-filtShape[1]))))
temp = zeros((halfWaveFilts[:,0].size,halfWaveFilts[0].size))
halfWaveFilts = maximum(halfWaveFilts,temp)
del temp
return halfWaveFilts
def intWindow(self, integratedFilts):
winlen = self.winLen
length = integratedFilts[0].size/winlen
mod = integratedFilts[0].size%winlen
outShortWindow = zeros((integratedFilts[:,0].size,length))
meanval = 0
if (mod != 0):
for i in xrange(integratedFilts[:,0].size):
mean(integratedFilts[i,0:-mod].reshape(length,winlen),1,out=outShortWindow[i])
else:
for i in xrange(integratedFilts[:,0].size):
mean(integratedFilts[i].reshape(length,winlen),1,out=outShortWindow[i])
del integratedFilts
return outShortWindow
def innerTest(self, window):
temper = copy(window)
sider = 7
st = .04
sizer = temper.size
inhibVal = 0
for j in xrange(sider):
inhibVal = (temper[0:j+sider+1].sum())*(sider*2+1)/(sider+1+j)
window[j] += - st*(inhibVal)
for j in xrange(sider,sizer - sider):
inhibVal = temper[j-sider:j+sider+1].sum()
window[j] += - st*(inhibVal)
for j in xrange(sizer-sider, sizer):
inhibVal = (temper[j-sider:sizer].sum())*(sider*2+1)/(sider+sizer-j)
window[j] += - st*(inhibVal)
maxsub = max(window) * self.maxOverallInhibit
window += - maxsub
del temper
return window
def outerTest(self, window):
newSatValue = scoreatpercentile(window, (76))
numones = where(window > newSatValue)
window[numones]=1
self.maxSatValue = newSatValue
del numones
return window
def makeHarmonicAmpMod(self, freq = 100, time = 1.,modulation=10, fsamp=None, numHarm=7):
if fsamp == None: fsamp = self.fs
samples = arange(time*fsamp)
signal = 0
for x in xrange(1,(numHarm+1),1):
signal = signal + sin(samples/float(fsamp)*x*freq*2*pi)
signal = (signal)*maximum(zeros(time*fsamp),sin((samples/float(fsamp)*modulation*2*pi)))
return signal
def makeSemiChordAmpMod(self, harmVec = None, freq=100, time = 1., modulation=10, fsamp=None, numHarm=7, semi = 2):
if (harmVec == None): harmVec = self.makeHarmonicAmpMod(freq,time,modulation,fsamp,numHarm)
if (semi == 0): return harmVec
return harmVec + self.makeHarmonicAmpMod(freq*(2**(semi/12.)),time,modulation,fsamp,numHarm)
I had installed the latest svn of numpy and the issue had vanished. I assume it was inside one of the numpy functions. I never got a chance to dig further into it.