Class MinkowskiToDenseTensor

MinkowskiEngine/MinkowskiOps.py:414–457

r"""Converts a (differentiable) sparse tensor to a torch tensor. The return type has the BxCxD1xD2x....xDN format. Example:: >>> dense_tensor = torch.rand(3, 4, 11, 11, 11, 11) # BxCxD1xD2x....xDN >>> dense_tensor.requires_grad = True >>> # Since the shape is fixed,

class MinkowskiToDenseTensor(MinkowskiModuleBase):
    r"""Converts a (differentiable) sparse tensor to a torch tensor.

    The return type has the BxCxD1xD2x....xDN format.

    Example::

       >>> dense_tensor = torch.rand(3, 4, 11, 11, 11, 11)  # BxCxD1xD2x....xDN
       >>> dense_tensor.requires_grad = True

       >>> # Since the shape is fixed, cache the coordinates for faster inference
       >>> coordinates = dense_coordinates(dense_tensor.shape)

       >>> network = nn.Sequential(
       >>>     # Add layers that can be applied on a regular pytorch tensor
       >>>     nn.ReLU(),
       >>>     MinkowskiToSparseTensor(coordinates=coordinates),
       >>>     MinkowskiConvolution(4, 5, stride=2, kernel_size=3, dimension=4),
       >>>     MinkowskiBatchNorm(5),
       >>>     MinkowskiReLU(),
       >>>     MinkowskiConvolutionTranspose(5, 6, stride=2, kernel_size=3, dimension=4),
       >>>     MinkowskiToDenseTensor(
       >>>         dense_tensor.shape
       >>>     ),  # must have the same tensor stride.
       >>> )

       >>> for i in range(5):
       >>>     print(f"Iteration: {i}")
       >>>     output = network(dense_tensor) # returns a regular pytorch tensor
       >>>     output.sum().backward()

    """

    def __init__(self, shape: torch.Size = None):
        MinkowskiModuleBase.__init__(self)
        self.shape = shape

    def forward(self, input: SparseTensor):
        # dense tensor to sparse tensor conversion
        dense_tensor, _, _ = input.dense(shape=self.shape)
        return dense_tensor

    def __repr__(self):
        return self.__class__.__name__ + "()"


class MinkowskiToFeature(MinkowskiModuleBase):