39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
from torch.autograd import Function
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from modules.functional.backend import _backend
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__all__ = ["nearest_neighbor_interpolate"]
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class NeighborInterpolation(Function):
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@staticmethod
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def forward(ctx, points_coords, centers_coords, centers_features):
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"""
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:param ctx:
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:param points_coords: coordinates of points, FloatTensor[B, 3, N]
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:param centers_coords: coordinates of centers, FloatTensor[B, 3, M]
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:param centers_features: features of centers, FloatTensor[B, C, M]
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:return:
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points_features: features of points, FloatTensor[B, C, N]
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"""
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centers_coords = centers_coords.contiguous()
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points_coords = points_coords.contiguous()
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centers_features = centers_features.contiguous()
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points_features, indices, weights = _backend.three_nearest_neighbors_interpolate_forward(
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points_coords, centers_coords, centers_features
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)
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ctx.save_for_backward(indices, weights)
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ctx.num_centers = centers_coords.size(-1)
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return points_features
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@staticmethod
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def backward(ctx, grad_output):
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indices, weights = ctx.saved_tensors
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grad_centers_features = _backend.three_nearest_neighbors_interpolate_backward(
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grad_output.contiguous(), indices, weights, ctx.num_centers
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)
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return None, None, grad_centers_features
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nearest_neighbor_interpolate = NeighborInterpolation.apply
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