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from torch import nn
from torch.autograd import Function
import torch
import importlib
import os
chamfer_found = importlib.find_loader("chamfer_2D") is not None
if not chamfer_found:
## Cool trick from https://github.com/chrdiller
print("Jitting Chamfer 2D")
cur_path = os.path.dirname(os.path.abspath(__file__))
build_path = cur_path.replace('chamfer2D', 'tmp')
os.makedirs(build_path, exist_ok=True)
from torch.utils.cpp_extension import load
chamfer_2D = load(name="chamfer_2D",
sources=[
"/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]),
"/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer2D.cu"]),
], build_directory=build_path)
print("Loaded JIT 2D CUDA chamfer distance")
else:
import chamfer_2D
print("Loaded compiled 2D CUDA chamfer distance")
# Chamfer's distance module @thibaultgroueix
# GPU tensors only
class chamfer_2DFunction(Function):
@staticmethod
def forward(ctx, xyz1, xyz2):
batchsize, n, dim = xyz1.size()
assert dim == 2, "Wrong last dimension for the chamfer distance 's input! Check with .size()"
_, m, dim = xyz2.size()
assert dim == 2, "Wrong last dimension for the chamfer distance 's input! Check with .size()"
device = xyz1.device
device = xyz1.device
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.to(device)
dist2 = dist2.to(device)
idx1 = idx1.to(device)
idx2 = idx2.to(device)
torch.cuda.set_device(device)
chamfer_2D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
return dist1, dist2, idx1, idx2
@staticmethod
def backward(ctx, graddist1, graddist2, gradidx1, gradidx2):
xyz1, xyz2, idx1, idx2 = ctx.saved_tensors
graddist1 = graddist1.contiguous()
graddist2 = graddist2.contiguous()
device = graddist1.device
gradxyz1 = torch.zeros(xyz1.size())
gradxyz2 = torch.zeros(xyz2.size())
gradxyz1 = gradxyz1.to(device)
gradxyz2 = gradxyz2.to(device)
chamfer_2D.backward(
xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2
)
return gradxyz1, gradxyz2
class chamfer_2DDist(nn.Module):
def __init__(self):
super(chamfer_2DDist, self).__init__()
def forward(self, input1, input2):
input1 = input1.contiguous()
input2 = input2.contiguous()
return chamfer_2DFunction.apply(input1, input2)
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