46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
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from torch.autograd import Function
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from third_party.pvcnn.functional.backend import _backend
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__all__ = ['trilinear_devoxelize']
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class TrilinearDevoxelization(Function):
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@staticmethod
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def forward(ctx, features, coords, resolution, is_training=True):
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"""
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:param ctx:
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:param coords: the coordinates of points, FloatTensor[B, 3, N]
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:param features: FloatTensor[B, C, R, R, R]
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:param resolution: int, the voxel resolution
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:param is_training: bool, training mode
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:return:
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FloatTensor[B, C, N]
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"""
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B, C = features.shape[:2]
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features = features.contiguous().view(B, C, -1)
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coords = coords[:,:3].contiguous()
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outs, inds, wgts = _backend.trilinear_devoxelize_forward(
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resolution, is_training, coords, features)
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if is_training:
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ctx.save_for_backward(inds, wgts)
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ctx.r = resolution
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return outs
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@staticmethod
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def backward(ctx, grad_output):
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"""
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:param ctx:
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:param grad_output: gradient of outputs, FloatTensor[B, C, N]
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:return:
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gradient of inputs, FloatTensor[B, C, R, R, R]
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"""
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inds, wgts = ctx.saved_tensors
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grad_inputs = _backend.trilinear_devoxelize_backward(
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grad_output.contiguous(), inds, wgts, ctx.r)
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return grad_inputs.view(grad_output.size(0), grad_output.size(1),
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ctx.r, ctx.r, ctx.r), None, None, None
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trilinear_devoxelize = TrilinearDevoxelization.apply
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