MinkowskiInterpolation

MinkowskiInterpolation

class MinkowskiEngine.MinkowskiInterpolation(return_kernel_map=False, return_weights=False)

Sample linearly interpolated features at the provided points.

__init__(return_kernel_map=False, return_weights=False)

Sample linearly interpolated features at the specified coordinates.

Args:

return_kernel_map (bool): In addition to the sampled features, the layer returns the kernel map as a pair of input row indices and output row indices. False by default.

return_weights (bool): When True, return the linear interpolation weights. False by default.

cpu() → T

Moves all model parameters and buffers to the CPU.

Returns:

Module: self

cuda(device: Optional[Union[int, torch.device]] = None) → T

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Arguments:

device (int, optional): if specified, all parameters will be

copied to that device

Returns:

Module: self

double() → T

Casts all floating point parameters and buffers to double datatype.

Returns:

Module: self

float() → T

Casts all floating point parameters and buffers to float datatype.

Returns:

Module: self

forward(input: MinkowskiSparseTensor.SparseTensor, tfield: torch.Tensor)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)

to(dtype, non_blocking=False)

to(tensor, non_blocking=False)

to(memory_format=torch.channels_last)

Its signature is similar to torch.Tensor.to(), but only accepts floating point desired dtype s. In addition, this method will only cast the floating point parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Args:

device (torch.device): the desired device of the parameters

and buffers in this module

dtype (torch.dtype): the desired floating point type of

the floating point parameters and buffers in this module

tensor (torch.Tensor): Tensor whose dtype and device are the desired

dtype and device for all parameters and buffers in this module

memory_format (torch.memory_format): the desired memory

format for 4D parameters and buffers in this module (keyword only argument)

Returns:

Module: self

Example:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

type(dst_type: Union[torch.dtype, str]) → T

Casts all parameters and buffers to dst_type.

Arguments:

dst_type (type or string): the desired type

Returns:

Module: self