Source code for pylops.torchoperator

__all__ = [

from typing import Optional

import numpy as np

from pylops import LinearOperator
from pylops.utils import deps

if deps.torch_enabled:
    from pylops._torchoperator import _TorchOperator
    torch_message = (
        "Torch package not installed. In order to be able to use"
        'the twoway module run "pip install torch" or'
        '"conda install -c pytorch torch".'
from pylops.utils.typing import TensorTypeLike

[docs]class TorchOperator(LinearOperator): """Wrap a PyLops operator into a Torch function. This class can be used to wrap a pylops operator into a torch function. Doing so, users can mix native torch functions (e.g. basic linear algebra operations, neural networks, etc.) and pylops operators. Since all operators in PyLops are linear operators, a Torch function is simply implemented by using the forward operator for its forward pass and the adjoint operator for its backward (gradient) pass. Parameters ---------- Op : :obj:`pylops.LinearOperator` PyLops operator batch : :obj:`bool`, optional Input has single sample (``False``) or batch of samples (``True``). If ``batch==False`` the input must be a 1-d Torch tensor or a tensor of size equal to ``Op.dims``; if ``batch==True`` the input must be a 2-d Torch tensor with batches along the first dimension or a tensor of size equal to ``[nbatch, *Op.dims]`` where ``nbatch`` is the size of the batch flatten : :obj:`bool`, optional Input is flattened along ``Op.dims`` (``True``) or not (``False``) device : :obj:`str`, optional Device to be used when applying operator (``cpu`` or ``gpu``) devicetorch : :obj:`str`, optional Device to be assigned the output of the operator to (any Torch-compatible device) """ def __init__( self, Op: LinearOperator, batch: bool = False, flatten: Optional[bool] = True, device: str = "cpu", devicetorch: str = "cpu", ) -> None: if not deps.torch_enabled: raise NotImplementedError(torch_message) self.device = device self.devicetorch = devicetorch super().__init__( dtype=np.dtype(Op.dtype), dims=Op.dims, dimsd=Op.dims, ) # define transpose indices to bring batch to last dimension before applying # pylops forward and adjoint (this will call matmat and rmatmat) self.transpf = np.roll(np.arange(2 if flatten else len(self.dims) + 1), -1) self.transpb = np.roll(np.arange(2 if flatten else len(self.dims) + 1), 1) if not batch: self.matvec = lambda x: Op @ x self.rmatvec = lambda x: Op.H @ x else: self.matvec = lambda x: (Op @ x.transpose(self.transpf)).transpose( self.transpb ) self.rmatvec = lambda x: (Op.H @ x.transpose(self.transpf)).transpose( self.transpb ) self.Top = _TorchOperator.apply def apply(self, x: TensorTypeLike) -> TensorTypeLike: """Apply forward pass to input vector Parameters ---------- x : :obj:`torch.Tensor` Input array Returns ------- y : :obj:`torch.Tensor` Output array resulting from the application of the operator to ``x``. """ return self.Top(x, self.matvec, self.rmatvec, self.device, self.devicetorch)