def set_num_points(self, pts):
self.num_points = pts
self.actual_number_of_points = pts
def randomize(self):
self.actual_number_of_points = min(
max(
np.random.randint(self.num_points * 0.8,
self.num_points * 1.2), 1),
self.points.shape[1],
)
if __name__ == "__main__":
from torchvision import transforms
import data_utils as d_utils
transforms = transforms.Compose([
d_utils.PointcloudToTensor(),
d_utils.PointcloudRotate(axis=np.array([1, 0, 0])),
d_utils.PointcloudScale(),
d_utils.PointcloudTranslate(),
d_utils.PointcloudJitter(),
])
dset = ModelNet40Cls(16, "./", train=True, transforms=transforms)
print(dset[0][0])
print(dset[0][1])
print(len(dset))
dloader = torch.utils.data.DataLoader(dset, batch_size=32, shuffle=True)
def set_num_points(self, pts):
self.num_points = pts
self.actual_number_of_points = pts
def randomize(self):
self.actual_number_of_points = min(
max(
np.random.randint(self.num_points * 0.8, self.num_points * 1.2),
1
), self.points.shape[1]
)
if __name__ == "__main__":
from torchvision import transforms
import data_utils as d_utils
transforms = transforms.Compose([
d_utils.PointcloudToTensor(),
d_utils.PointcloudRotate(x_axis=True),
d_utils.PointcloudScale(),
d_utils.PointcloudTranslate(),
d_utils.PointcloudJitter()
])
dset = ModelNet40Cls(16, "./", train=True, transforms=transforms)
print(dset[0][0])
print(dset[0][1])
print(len(dset))
dloader = torch.utils.data.DataLoader(dset, batch_size=32, shuffle=True)
