def get_scaledLaplacian(coord,neighborNum,pointNumber):
coord = coord[0]
tree = cKDTree(coord)
dd, ii = tree.query(coord, k=neighborNum)
A = adjacency(dd, ii)
scaledLaplacian = scaled_laplacian(A)
flattenLaplacian = scaledLaplacian.tolil().reshape((1, pointNumber * pointNumber))
return flattenLaplacian
def prepareGraph(inputData, neighborNumber, pointNumber, dataType):
scaledLaplacianDict = dict()
#baseDir = os.path.dirname(os.path.abspath(__file__))
# baseDir ='/raid60/yingxue.zhang2/ICASSP_code'
baseDir = os.path.abspath(os.path.dirname(os.getcwd()))
if para.dataset == 'ModelNet40':
fileDir = baseDir + '/graph/' + dataType + '_pn_' + str(
pointNumber) + '_nn_' + str(neighborNumber)
elif para.dataset == 'ModelNet10':
fileDir = baseDir + '/graph_ModelNet10/' + dataType + '_pn_' + str(
pointNumber) + '_nn_' + str(neighborNumber)
else:
print("Please enter a valid dataset")
if (not os.path.isdir(fileDir)):
print("calculating the graph data")
os.makedirs(fileDir)
print('>> Prepare graph. Batches count: {}'.format(len(inputData)))
for batchIndex in range(len(inputData)):
batchInput = inputData[batchIndex]
for i in range(len(batchInput)):
print(i)
pcCoordinates = batchInput[i]
tree = cKDTree(pcCoordinates)
dd, ii = tree.query(pcCoordinates, k=neighborNumber)
A = adjacency(dd, ii)
scaledLaplacian = scaled_laplacian(A)
flattenLaplacian = scaledLaplacian.tolil().reshape(
(1, pointNumber * pointNumber))
if i == 0:
batchFlattenLaplacian = flattenLaplacian
else:
batchFlattenLaplacian = scipy.sparse.vstack(
[batchFlattenLaplacian, flattenLaplacian])
scaledLaplacianDict.update({batchIndex: batchFlattenLaplacian})
with open(fileDir + '/batchGraph_' + str(batchIndex),
'wb') as handle:
pickle.dump(batchFlattenLaplacian, handle)
# files.download(handle)
print("Saving the graph data batch" + str(batchIndex))
else:
print("Loading the graph data from " + dataType + 'Data')
scaledLaplacianDict = loadGraph(inputData, neighborNumber, pointNumber,
fileDir)
return scaledLaplacianDict
def prepareGraph(inputData, neighborNumber, pointNumber, dataType):
#Description: generate graph structure and store in the system for reuse
#input: (1)inputData: input point coordinates (2)neighborNumber: neighbor number when constructing nearest neighbor graph
# (3)pointNumber: point number of each object (4)dataType: training set or testing set
#return: scaled Laplacian matrix of each object
scaledLaplacianDict = dict()
#baseDir = os.path.dirname(os.path.abspath(__file__))
#baseDir ='/raid60/yingxue.zhang2/ICASSP_code'
baseDir = os.path.abspath(os.path.dirname(os.getcwd()))
fileDir = baseDir + '/graph/' + dataType + '_pn_' + str(
pointNumber) + '_nn_' + str(neighborNumber)
if (not os.path.isdir(fileDir)):
print "calculating the graph data"
os.makedirs(fileDir)
for batchIndex in range(len(inputData)):
batchInput = inputData[batchIndex]
for i in range(len(batchInput)):
print i
pcCoordinates = batchInput[i]
tree = cKDTree(pcCoordinates)
dd, ii = tree.query(pcCoordinates, k=neighborNumber)
A = adjacency(dd, ii)
scaledLaplacian = scaled_laplacian(A)
flattenLaplacian = scaledLaplacian.tolil().reshape(
(1, pointNumber * pointNumber))
if i == 0:
batchFlattenLaplacian = flattenLaplacian
else:
batchFlattenLaplacian = scipy.sparse.vstack(
[batchFlattenLaplacian, flattenLaplacian])
scaledLaplacianDict.update({batchIndex: batchFlattenLaplacian})
with open(fileDir + '/batchGraph_' + str(batchIndex),
'wb') as handle:
pickle.dump(batchFlattenLaplacian, handle)
print "Saving the graph data batch" + str(batchIndex)
else:
print("Loading the graph data from " + dataType + 'Data')
scaledLaplacianDict = loadGraph(inputData, neighborNumber, pointNumber,
fileDir)
return scaledLaplacianDict
def prepareGraph(inputData, neighborNumber, pointNumber, dataType):
scaledLaplacianDict = dict()
#baseDir = os.path.dirname(os.path.abspath(__file__))
baseDir ='/raid60/yingxue.zhang2/ICASSP_code'
#baseDir= os.path.abspath(os.path.dirname(os.getcwd()))
if para.dataset == 'ModelNet40':
fileDir = baseDir+ '/graph/' + dataType+'_pn_'+str(pointNumber)+'_nn_'+str(neighborNumber)
elif para.dataset == 'ModelNet10':
fileDir = baseDir+ '/graph_ModelNet10/' + dataType+'_pn_'+str(pointNumber)+'_nn_'+str(neighborNumber)
else:
print "Please enter a valid dataset"
if (not os.path.isdir(fileDir)):
print "calculating the graph data"
os.makedirs(fileDir)
for batchIndex in range(len(inputData)):
batchInput = inputData[batchIndex]
for i in range(len(batchInput)):
print i
pcCoordinates = batchInput[i]
tree = cKDTree(pcCoordinates)
dd, ii = tree.query(pcCoordinates, k = neighborNumber)
A = adjacency(dd, ii)
scaledLaplacian = scaled_laplacian(A)
flattenLaplacian = scaledLaplacian.tolil().reshape((1, pointNumber*pointNumber))
if i ==0:
batchFlattenLaplacian = flattenLaplacian
else:
batchFlattenLaplacian = scipy.sparse.vstack([batchFlattenLaplacian, flattenLaplacian])
scaledLaplacianDict.update({batchIndex: batchFlattenLaplacian})
with open(fileDir+'/batchGraph_'+str(batchIndex), 'wb') as handle:
pickle.dump(batchFlattenLaplacian, handle)
print "Saving the graph data batch"+str(batchIndex)
else:
print("Loading the graph data from "+dataType+'Data')
scaledLaplacianDict = loadGraph(inputData, neighborNumber, pointNumber, fileDir)
return scaledLaplacianDict
def prepareGraph(inputData, neighborNumber, pointNumber, dataType):
#Description: generate graph structure and store in the system for reuse
#input: (1)inputData: input point coordinates (2)neighborNumber: neighbor number when constructing nearest neighbor graph
# (3)pointNumber: point number of each object (4)dataType: training set or testing set
#return: scaled Laplacian matrix of each object
scaledLaplacianDict = dict()
#baseDir = os.path.dirname(os.path.abspath(__file__))
baseDir ='/raid60/yingxue.zhang2/ICASSP_code'
#baseDir= os.path.abspath(os.path.dirname(os.getcwd()))
fileDir = baseDir+ '/graph/' + dataType+'_pn_'+str(pointNumber)+'_nn_'+str(neighborNumber)
if (not os.path.isdir(fileDir)):
print "calculating the graph data"
os.makedirs(fileDir)
for batchIndex in range(len(inputData)):
batchInput = inputData[batchIndex]
for i in range(len(batchInput)):
print i
pcCoordinates = batchInput[i]
tree = cKDTree(pcCoordinates)
dd, ii = tree.query(pcCoordinates, k = neighborNumber)
A = adjacency(dd, ii)
scaledLaplacian = scaled_laplacian(A)
flattenLaplacian = scaledLaplacian.tolil().reshape((1, pointNumber*pointNumber))
if i ==0:
batchFlattenLaplacian = flattenLaplacian
else:
batchFlattenLaplacian = scipy.sparse.vstack([batchFlattenLaplacian, flattenLaplacian])
scaledLaplacianDict.update({batchIndex: batchFlattenLaplacian})
with open(fileDir+'/batchGraph_'+str(batchIndex), 'wb') as handle:
pickle.dump(batchFlattenLaplacian, handle)
print "Saving the graph data batch"+str(batchIndex)
else:
print("Loading the graph data from "+dataType+'Data')
scaledLaplacianDict = loadGraph(inputData, neighborNumber, pointNumber, fileDir)
return scaledLaplacianDict