a3b9a74fb0b6cf75bc81efdc42804f07709f87d1,torch_geometric/utils/mnist_monet.py,,,#,66

Before Change

for i in range(2):
    a = polar2euclidean(rho[i], theta[i])
    print(a.size())

// from __future__ import division

After Change

input, target, rho, theta = data
target = target.byte()

positions = []
for i in range(70000):
    adj = polar2euclidean(rho[i], theta[i])
    adj = sparse_to_dict(adj)
    pos = dict_to_pos(adj)
    minimum, _ = pos.min(dim=0)
    pos = pos - minimum
    positions.append(pos)

positions = torch.stack(positions, dim=0)

torch.save(positions, os.path.join(path, "positions.pt"))

Instances