def getModelSize(self):
'''
Function to get aimed model size
'''
nnzW, sizeW, sparseW = utils.countnnZ(self.bonsaiObj.W, self.sW)
nnzV, sizeV, sparseV = utils.countnnZ(self.bonsaiObj.V, self.sV)
nnzT, sizeT, sparseT = utils.countnnZ(self.bonsaiObj.T, self.sT)
totalnnZ = (nnzT + nnzV + nnzW)
totalSize = (sizeW + sizeV + sizeT)
hasSparse = (sparseW or sparseV or sparseT)
return totalnnZ, totalSize, hasSparse
def getModelSize(matrixList, sparcityList, expected=True, bytesPerVar=4):
'''
expected: Expected size according to the parameters set. The number of
zeros could actually be more than that is required to satisfy the
sparsity constraint.
'''
nnzList, sizeList, isSparseList = [], [], []
hasSparse = False
for i in range(len(matrixList)):
A, s = matrixList[i], sparcityList[i]
assert A.ndim == 2
assert s >= 0
assert s <= 1
nnz, size, sparse = utils.countnnZ(A, s, bytesPerVar=bytesPerVar)
nnzList.append(nnz)
sizeList.append(size)
hasSparse = (hasSparse or sparse)
totalnnZ = np.sum(nnzList)
totalSize = np.sum(sizeList)
if expected:
return totalnnZ, totalSize, hasSparse
numNonZero = 0
totalSize = 0
hasSparse = False
for i in range(len(matrixList)):
A, s = matrixList[i], sparcityList[i]
numNonZero_ = np.count_nonzero(A)
numNonZero += numNonZero_
hasSparse = (hasSparse or (s < 0.5))
if s <= 0.5:
totalSize += numNonZero_ * 2 * bytesPerVar
else:
totalSize += A.size * bytesPerVar
return numNonZero, totalSize, hasSparse
def getModelSize(self):
'''
Function to get aimed model size
'''
totalnnZ = 0
totalSize = 0
hasSparse = False
for i in range(0, self.numMatrices[0]):
nnz, size, sparseFlag = utils.countnnZ(self.FastParams[i], self.sW)
totalnnZ += nnz
totalSize += size
hasSparse = hasSparse or sparseFlag
for i in range(self.numMatrices[0], self.totalMatrices):
nnz, size, sparseFlag = utils.countnnZ(self.FastParams[i], self.sU)
totalnnZ += nnz
totalSize += size
hasSparse = hasSparse or sparseFlag
for i in range(self.totalMatrices, len(self.FastParams)):
nnz, size, sparseFlag = utils.countnnZ(self.FastParams[i], 1.0)
totalnnZ += nnz
totalSize += size
hasSparse = hasSparse or sparseFlag
# Replace this with classifier class call
nnz, size, sparseFlag = utils.countnnZ(self.FC, 1.0)
totalnnZ += nnz
totalSize += size
hasSparse = hasSparse or sparseFlag
nnz, size, sparseFlag = utils.countnnZ(self.FCbias, 1.0)
totalnnZ += nnz
totalSize += size
hasSparse = hasSparse or sparseFlag
return totalnnZ, totalSize, hasSparse
