def forward(ctx, xyz1, xyz2):
batchsize, n, _ = xyz1.size()
_, m, _ = xyz2.size()
dist1 = torch.zeros(batchsize, n)
dist2 = torch.zeros(batchsize, m)
idx1 = torch.zeros(batchsize, n).type(torch.IntTensor)
idx2 = torch.zeros(batchsize, m).type(torch.IntTensor)
dist1 = dist1.cuda()
dist2 = dist2.cuda()
idx1 = idx1.cuda()
idx2 = idx2.cuda()
chamfer.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
return dist1, dist2
def forward(ctx, opt, p1, p2):
batch_size = p1.shape[0]
num_p1_points = p1.shape[1]
num_p2_points = p2.shape[1]
dist1 = torch.zeros(batch_size, num_p1_points, device=opt.device)
dist2 = torch.zeros(batch_size, num_p2_points, device=opt.device)
idx1 = torch.zeros(batch_size,
num_p1_points,
dtype=torch.int32,
device=opt.device)
idx2 = torch.zeros(batch_size,
num_p2_points,
dtype=torch.int32,
device=opt.device)
p1 = p1.contiguous()
p2 = p2.contiguous()
if "cuda" in opt.device:
chamfer.forward(p1, p2, dist1, dist2, idx1, idx2)
else:
raise NotImplementedError("CPU version not implemented")
ctx.opt = opt
ctx.save_for_backward(p1, p2, dist1, dist2, idx1, idx2)
return dist1, dist2
import sys
import os
for file in os.listdir("build"):
if file.startswith("lib"):
sys.path.insert(0, os.path.join("build", file))
# torch must be imported before we import chamfer
import torch
import chamfer
batch_size = 8
n, m = 30, 20
xyz1 = torch.rand((batch_size, n, 3)).cuda()
xyz2 = torch.rand((batch_size, m, 3)).cuda()
dist1 = torch.zeros(batch_size, n).cuda()
dist2 = torch.zeros(batch_size, m).cuda()
idx1 = torch.zeros((batch_size, n), dtype=torch.int).cuda()
idx2 = torch.zeros((batch_size, m), dtype=torch.int).cuda()
chamfer.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
print(dist1)
print(dist2)
print(idx1)
print(idx2)
def forward(ctx, xyz1, xyz2):
dist1, dist2, idx1, idx2 = chamfer.forward(xyz1, xyz2)
ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
return dist1, dist2
