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import torch
from torch.autograd import Function
from pointops._C import interpolation_forward_cuda, interpolation_backward_cuda
from .query import knn_query
def interpolation(xyz, new_xyz, feat, offset, new_offset, k=3):
"""
input: coords: (m, 3), new_xyz: (n, 3), color: (m, c), offset: (b), new_offset: (b)
output: (n, c)
"""
assert xyz.is_contiguous() and new_xyz.is_contiguous() and feat.is_contiguous()
idx, dist = knn_query(k, xyz, offset, new_xyz, new_offset) # (n, 3), (n, 3)
dist_recip = 1.0 / (dist + 1e-8) # (n, 3)
norm = torch.sum(dist_recip, dim=1, keepdim=True)
weight = dist_recip / norm # (n, 3)
new_feat = torch.cuda.FloatTensor(new_xyz.shape[0], feat.shape[1]).zero_()
for i in range(k):
new_feat += feat[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1)
return new_feat
class Interpolation(Function):
@staticmethod
def forward(ctx, xyz, new_xyz, input, offset, new_offset, k=3):
"""
input: coords: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b)
output: (n, c)
"""
assert xyz.is_contiguous() and new_xyz.is_contiguous() and input.is_contiguous()
idx, dist = knn_query(k, xyz, offset, new_xyz, new_offset) # (n, k), (n, k)
dist_recip = 1.0 / (dist + 1e-8) # (n, k)
norm = torch.sum(dist_recip, dim=1, keepdim=True)
weight = dist_recip / norm # (n, k)
n, c, m = new_xyz.shape[0], input.shape[1], input.shape[0]
output = torch.cuda.FloatTensor(n, c).zero_()
interpolation_forward_cuda(n, c, k, input, idx, weight, output)
ctx.m, ctx.k = m, k
ctx.save_for_backward(idx, weight)
return output
@staticmethod
def backward(ctx, grad_output):
"""
input: coords: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b)
output: (n, c)
"""
m, k = ctx.m, ctx.k
idx, weight = ctx.saved_tensors
n, c = grad_output.shape
grad_input = torch.cuda.FloatTensor(m, c).zero_()
interpolation_backward_cuda(n, c, k, grad_output, idx, weight, grad_input)
return None, None, grad_input, None, None, None
interpolation2 = Interpolation.apply
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