LION/third_party/pvcnn/functional/interpolatation.py

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2023-01-23 05:14:49 +00:00
from torch.autograd import Function
# from modules.functional.backend import _backend
from third_party.pvcnn.functional.backend import _backend
import torch
from torch.cuda.amp import autocast, GradScaler, custom_fwd, custom_bwd
__all__ = ['nearest_neighbor_interpolate']
class NeighborInterpolation(Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx, points_coords, centers_coords, centers_features):
"""
:param ctx:
:param points_coords: coordinates of points, FloatTensor[B, 3, N]
:param centers_coords: coordinates of centers, FloatTensor[B, 3, M]
:param centers_features: features of centers, FloatTensor[B, C, M]
:return:
points_features: features of points, FloatTensor[B, C, N]
"""
centers_coords = centers_coords[:,:3].contiguous()
points_coords = points_coords[:,:3].contiguous()
centers_features = centers_features.contiguous()
points_features, indices, weights = _backend.three_nearest_neighbors_interpolate_forward(
points_coords, centers_coords, centers_features)
ctx.save_for_backward(indices, weights)
ctx.num_centers = centers_coords.size(-1)
return points_features
@staticmethod
@custom_bwd
def backward(ctx, grad_output):
indices, weights = ctx.saved_tensors
grad_centers_features = _backend.three_nearest_neighbors_interpolate_backward(
grad_output.contiguous(), indices, weights, ctx.num_centers)
return None, None, grad_centers_features
nearest_neighbor_interpolate = NeighborInterpolation.apply
#def nearest_neighbor_interpolate(points_coords, centers_coords, centers_features):
# # points_coords: (B,6, 64)
# # centers_coords: (B,6, 16)
# # centers_features: (B,128,16)
# # interpolated_features: (B,128,64)
# B = points_coords.shape[0]
# D = centers_features.shape[1]
# N = points_coords.shape[2]
# output = torch.zeros(B,D,N).to(points_coords.shape)
# for b in range(B):
# for n in range(N):
# points_coords_cur = points_coords