def getAlpha_mpg(M, Dict): # GPU
PATCh_SIZE = 16
IMAGE_SIZE = 256
kin = 32
minabsin = 0
X = im2col(M, (PATCh_SIZE, PATCh_SIZE))
X = X.transpose()
A = np.empty([58081, 256])
A_gpu1 = mpg.mp(Dict.transpose(), X[0:32000, :], k=kin, minabs=minabsin)
A_gpu2 = mpg.mp(Dict.transpose(), X[32000:58080, :], k=kin, minabs=minabsin)
A[0:32000, :] = A_gpu1
A[32000:58080, :] = A_gpu2
Alpha = np.dot(A, Dict.transpose())
A[58080, :] = X[58080, :]
result = col2im(Alpha.transpose(), [IMAGE_SIZE, IMAGE_SIZE], (PATCh_SIZE, PATCh_SIZE))
return result
def main():
"""Times various versions of Matching Pursuit."""
nshort = 6
tshort = 2
nmed = 3
tmed = 6
nlong = 1
#Setup variables for inference
numDict = int(9600)
numBatch = int(1024)
dataSize = int(256)
dictsIn = np.random.randn(numDict,dataSize)
dictsIn = (np.sqrt(np.diag(1/np.diag(dictsIn.dot(dictsIn.T))))).dot(dictsIn)
stimuli = np.random.randn(numBatch,dataSize)
kin = 32
minabsin = 0.
#MP
params = """Parameters:
numDict: """+str(numDict)+"""
numBatch: """+str(numBatch)+"""
dataSize: """+str(dataSize)+"""
k-sparseness: """+str(kin)+"""\n"""
print params
start = timer()
mpn.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = timer()-start
if dt < tshort:
for ii in xrange(nshort-1):
start = timer()
mpn.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = dt+timer()-start
num = nshort
dt = dt/(nshort)
elif dt < tmed:
for ii in xrange(nmed-1):
start = timer()
mpn.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = dt+timer()-start
num = nmed
dt = dt/(nmed)
else:
for ii in xrange(nlong-1):
start = timer()
mpn.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = dt+timer()-start
num = nlong
dt = dt/(nlong)
print '---------------Numpy based MP---------------'
print 'Average time over '+str(num)+' trials:'
print '%f s' % dt
start = timer()
mpg.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = timer()-start
if dt < tshort:
for ii in xrange(nshort-1):
start = timer()
mpg.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = dt+timer()-start
num = nshort
dt = dt/(nshort)
elif dt < tmed:
for ii in xrange(nmed-1):
start = timer()
mpg.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = dt+timer()-start
num = nmed
dt = dt/(nmed)
else:
for ii in xrange(nlong-1):
start = timer()
mpg.mp(dictsIn,stimuli,k=kin,minabs=minabsin)
dt = dt+timer()-start
num = nlong
dt = dt/(nlong)
print '----------NumbaPro GPU based MP---------'
print 'Average time over '+str(num)+' trials:'
print '%f s' % dt
def test_numbaprog(self):
dictionary = self.rng.randn(self.numDict,self.dataSize)
dictionary = np.array((np.sqrt(np.diag(1/np.diag(dictionary.dot(dictionary.T))))).dot(dictionary),dtype=np.float32,order='F')
stimuli = np.array(self.rng.randn(self.numBatch,self.dataSize),dtype=np.float32,order='F')
c = mpg.mp(dictionary,stimuli)
assert np.allclose(c.dot(dictionary),stimuli,atol=1e-5)
