use with my own data

[SabraHashemi]

as you mention in readme i changed source and target to my owndata:

def main():
	args = options()
	torch.backends.cudnn.deterministic = True

	if args.user_data:
		template = np.array(o3d.io.read_point_cloud("/home/fteam4/sabra/catalyst/probreg/9.ply"))#np.random.randn(1, 100, 3)					# Define/Read template point cloud. [Shape: BATCH x No. of Points x 3]

		source =np.array(o3d.io.read_point_cloud("/home/fteam4/sabra/catalyst/probreg/9.ply")) #np.random.randn(1, 75, 3)						# Define/Read source point cloud. [Shape: BATCH x No. of Points x 3]
		mask = np.zeros((1, 100, 1))							# Define/Read mask for point cloud. [Not mandatory in testing]
		igt = np.zeros((1, 4, 4))								# Define/Read igt transformation. [Not mandatory during testing]
		testset = UserData(template, source, mask=None, igt=None)	

but I got :

  File "/home/fteam4/sabra/catalyst/masknet-main/test.py", line 196, in <module>
    main()
  File "/home/fteam4/sabra/catalyst/masknet-main/test.py", line 163, in main
    testset = UserData(template, source, mask=None, igt=None)	
  File "/home/fteam4/sabra/catalyst/masknet-main/learning3d/data_utils/dataloaders.py", line 334, in __init__
    self.check_dataset()
  File "/home/fteam4/sabra/catalyst/masknet-main/learning3d/data_utils/dataloaders.py", line 337, in check_dataset
    if len(self.template)>2:
TypeError: len() of unsized object

[SabraHashemi]

i solved this but still i got an error

n/learning3d/models/pointnet.py", line 57, in forward input_data = input_data.permute(0, 2, 1) RuntimeError: number of dims don't match in permute