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import torch
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['gather_points_forward', 'gather_points_backward'])
class GatherPoints(Function):
"""Gather points with given index."""
@staticmethod
def forward(ctx, features: torch.Tensor,
indices: torch.Tensor) -> torch.Tensor:
"""
Args:
features (Tensor): (B, C, N) features to gather.
indices (Tensor): (B, M) where M is the number of points.
Returns:
Tensor: (B, C, M) where M is the number of points.
"""
assert features.is_contiguous()
assert indices.is_contiguous()
B, npoint = indices.size()
_, C, N = features.size()
output = torch.cuda.FloatTensor(B, C, npoint)
ext_module.gather_points_forward(
features, indices, output, b=B, c=C, n=N, npoints=npoint)
ctx.for_backwards = (indices, C, N)
if torch.__version__ != 'parrots':
ctx.mark_non_differentiable(indices)
return output
@staticmethod
def backward(ctx, grad_out):
idx, C, N = ctx.for_backwards
B, npoint = idx.size()
grad_features = torch.cuda.FloatTensor(B, C, N).zero_()
grad_out_data = grad_out.data.contiguous()
ext_module.gather_points_backward(
grad_out_data,
idx,
grad_features.data,
b=B,
c=C,
n=N,
npoints=npoint)
return grad_features, None
gather_points = GatherPoints.apply
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