55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
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from torch.autograd import Function
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# from modules.functional.backend import _backend
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from third_party.pvcnn.functional.backend import _backend
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import torch
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from torch.cuda.amp import autocast, GradScaler, custom_fwd, custom_bwd
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__all__ = ['nearest_neighbor_interpolate']
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class NeighborInterpolation(Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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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[:,:3].contiguous()
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points_coords = points_coords[:,:3].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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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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@custom_bwd
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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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return None, None, grad_centers_features
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nearest_neighbor_interpolate = NeighborInterpolation.apply
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#def nearest_neighbor_interpolate(points_coords, centers_coords, centers_features):
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# # points_coords: (B,6, 64)
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# # centers_coords: (B,6, 16)
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# # centers_features: (B,128,16)
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# # interpolated_features: (B,128,64)
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# B = points_coords.shape[0]
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# D = centers_features.shape[1]
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# N = points_coords.shape[2]
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# output = torch.zeros(B,D,N).to(points_coords.shape)
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# for b in range(B):
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# for n in range(N):
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# points_coords_cur = points_coords
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