83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
from torch import nn
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from torch.autograd import Function
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import torch
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import importlib
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import os
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chamfer_found = importlib.find_loader("chamfer_5D") is not None
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if not chamfer_found:
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## Cool trick from https://github.com/chrdiller
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print("Jitting Chamfer 5D")
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cur_path = os.path.dirname(os.path.abspath(__file__))
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build_path = cur_path.replace('chamfer5D', 'tmp')
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os.makedirs(build_path, exist_ok=True)
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from torch.utils.cpp_extension import load
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chamfer_5D = load(name="chamfer_5D",
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sources=[
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"/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]),
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"/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer5D.cu"]),
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], build_directory=build_path)
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print("Loaded JIT 5D CUDA chamfer distance")
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else:
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import chamfer_5D
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print("Loaded compiled 5D CUDA chamfer distance")
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# Chamfer's distance module @thibaultgroueix
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# GPU tensors only
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class chamfer_5DFunction(Function):
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@staticmethod
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def forward(ctx, xyz1, xyz2):
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batchsize, n, dim = xyz1.size()
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assert dim==5, "Wrong last dimension for the chamfer distance 's input! Check with .size()"
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_, m, dim = xyz2.size()
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assert dim==5, "Wrong last dimension for the chamfer distance 's input! Check with .size()"
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device = xyz1.device
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device = xyz1.device
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dist1 = torch.zeros(batchsize, n)
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dist2 = torch.zeros(batchsize, m)
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idx1 = torch.zeros(batchsize, n).type(torch.IntTensor)
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idx2 = torch.zeros(batchsize, m).type(torch.IntTensor)
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dist1 = dist1.to(device)
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dist2 = dist2.to(device)
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idx1 = idx1.to(device)
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idx2 = idx2.to(device)
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torch.cuda.set_device(device)
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chamfer_5D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
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ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
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return dist1, dist2, idx1, idx2
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@staticmethod
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def backward(ctx, graddist1, graddist2, gradidx1, gradidx2):
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xyz1, xyz2, idx1, idx2 = ctx.saved_tensors
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graddist1 = graddist1.contiguous()
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graddist2 = graddist2.contiguous()
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device = graddist1.device
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gradxyz1 = torch.zeros(xyz1.size())
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gradxyz2 = torch.zeros(xyz2.size())
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gradxyz1 = gradxyz1.to(device)
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gradxyz2 = gradxyz2.to(device)
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chamfer_5D.backward(
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xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2
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)
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return gradxyz1, gradxyz2
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class chamfer_5DDist(nn.Module):
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def __init__(self):
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super(chamfer_5DDist, self).__init__()
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def forward(self, input1, input2):
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input1 = input1.contiguous()
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input2 = input2.contiguous()
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return chamfer_5DFunction.apply(input1, input2)
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