LION/third_party/ChamferDistancePytorch/chamfer3D/dist_chamfer_3D.py
2023-01-23 00:14:49 -05:00

134 lines
4.5 KiB
Python

from torch import nn
from torch.autograd import Function
import torch
import importlib
import os
from torch.cuda.amp import autocast, GradScaler, custom_fwd, custom_bwd
cur_path = os.path.dirname(os.path.abspath(__file__))
build_path = cur_path.replace('chamfer3D', 'tmp')
os.makedirs(build_path, exist_ok=True)
from torch.utils.cpp_extension import load
chamfer_3D = load(name="chamfer_3D",
sources=[
"/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]),
"/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer3D.cu"]),
], build_directory=build_path)
#chamfer_found = importlib.find_loader("chamfer_3D") is not None
#if not chamfer_found:
# ## Cool trick from https://github.com/chrdiller
# print("Jitting Chamfer 3D")
# cur_path = os.path.dirname(os.path.abspath(__file__))
# build_path = cur_path.replace('chamfer3D', 'tmp')
# os.makedirs(build_path, exist_ok=True)
#
# from torch.utils.cpp_extension import load
# chamfer_3D = load(name="chamfer_3D",
# sources=[
# "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]),
# "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer3D.cu"]),
# ], build_directory=build_path)
# print("Loaded JIT 3D CUDA chamfer distance")
#
#else:
# import chamfer_3D
# print("Loaded compiled 3D CUDA chamfer distance")
# Chamfer's distance module @thibaultgroueix
# GPU tensors only
class chamfer_3DFunction(Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx, xyz1, xyz2):
batchsize, n, dim = xyz1.size()
assert dim==3, "Wrong last dimension for the chamfer distance 's input! Check with .size()"
_, m, dim = xyz2.size()
assert dim==3, "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_3D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
return dist1, dist2, idx1, idx2
@staticmethod
@custom_bwd
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_3D.backward(
xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2
)
return gradxyz1, gradxyz2
class chamfer_3DDist(nn.Module):
def __init__(self):
super(chamfer_3DDist, self).__init__()
def forward(self, input1, input2):
input1 = input1.contiguous()
input2 = input2.contiguous()
return chamfer_3DFunction.apply(input1, input2)
# Chamfer's distance module @thibaultgroueix
# GPU tensors only
class chamfer_3DFunction_noGrad(Function):
@staticmethod
def forward(ctx, xyz1, xyz2):
batchsize, n, dim = xyz1.size()
assert dim==3, "Wrong last dimension for the chamfer distance 's input! Check with .size()"
_, m, dim = xyz2.size()
assert dim==3, "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_3D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
return dist1, dist2, idx1, idx2
class chamfer_3DDist_nograd(nn.Module):
def __init__(self):
super(chamfer_3DDist_nograd, self).__init__()
def forward(self, input1, input2):
input1 = input1.contiguous()
input2 = input2.contiguous()
return chamfer_3DFunction_noGrad.apply(input1, input2)