I was unable to complete compilation

[jiepainanhai]

platform is windows11,pycharm2022.1(professional.)
cuda toolkit11.3, pytorch1.11.0 ， Driver Version: 472.12（RTX3060Ti） CUDA Version: 11.4
This is an error message
error_info.txt

[ar4]

Hello and thank you for reporting the error that you encountered. It looks like you might be trying to install an older version of Deepwave. Are you able to use a more recent release? Version v0.0.19 and later do not generally require the user to compile the code, which should avoid problems like the one that you encountered. If you can, I recommend trying to install the latest version of Deepwave. If you need to install a version before v0.0.10 (when the Deepwave interface was different) in order to use some existing code, then I think there are two possible options: you could try installing instead on Linux, or I could provide you with a wrapper to convert calls to the old version of Deepwave into ones compatible with the new version of Deepwave.

[jiepainanhai]

I'm using deepwave version 0.0.10, in order to use the 'Propagator' function, I still don't understand how it works for the new version, so it's a good idea to use a wrapper

[ar4]

If you wish to use the old "Propagator" interface, then you need to use v0.0.9 or earlier - the interface changed to the new one in v0.0.10 (and has remained the same since then).

I think this wrapper should work to convert from old-style calls to new ones:

import torch
import deepwave


class Propagator():

    def __init__(self, model, dx, pml_width=None, survey_pad=None, vpmax=None):
        """Wrapper to call Deepwave's scalar propagator

        Args:
            model: A dictionary containing a 'vp' key whose value is a
                [ny, nx] shape Float Tensor containing the velocity model.
            dx: A float or list of floats containing cell spacing in each
                dimension.
            pml_width: An int or list of ints specifying number of cells to use
                for the PML. This will be added to the beginning and end of each
                propagating dimension. If provided as a list, it should be of
                length 6, with each sequential group of two integer elements
                referring to the beginning and end PML width for a dimension.
                The last two entries are ignored. Optional, default 20.
            survey_pad: A float or None, or list of such with 2 elements for each
                dimension, specifying the padding (in units of dx) to add.
                In each dimension, the survey (wave propagation) area for each
                batch of shots will be from the left-most source/receiver minus
                the left survey_pad, to the right-most source/receiver plus the
                right survey pad, over all shots in the batch, or to the edges of
                the model, whichever comes first. If a list, it specifies the
                left and right survey_pad in each dimension. If None, the survey
                area will continue to the edges of the model. If a float, that
                value will be used on the left and right of each dimension.
                Optional, default None.
            vpmax: A float specifying the velocity to use when calculating the
                internal time step size using the CFL condition.
                Optional, default None which will use the maximum in the
                provided model.
        """
        if 'vp' not in model:
            raise RuntimeError("model should contain a 'vp' key")

        if not isinstance(model['vp'], torch.Tensor):
            raise RuntimeError("model should be a Tensor")

        if not model['vp'].ndim == 2:
            raise RuntimeError("model should have two dimensions")

        if isinstance(pml_width, list):
            pml_width = pml_width[:4]

        self.vp = model['vp']
        self.dx = dx
        self.pml_width = pml_width
        self.survey_pad = survey_pad
        self.vpmax = vpmax

    def __call__(self, source_amplitudes, source_locations, receiver_locations,
                 dt):
        if isinstance(self.dx, list):
            source_locations[..., 0] /= self.dx[0]
            source_locations[..., 1] /= self.dx[1]
            receiver_locations[..., 0] /= self.dx[0]
            receiver_locations[..., 1] /= self.dx[1]
        else:
            source_locations /= self.dx
            receiver_locations /= self.dx
        return deepwave.scalar(self.vp,
                               self.dx,
                               dt,
                               source_amplitudes=source_amplitudes.movedim(
                                   0, -1),
                               source_locations=source_locations.long(),
                               receiver_locations=receiver_locations.long(),
                               pml_width=self.pml_width,
                               survey_pad=self.survey_pad,
                               max_vel=self.vpmax)[-1].movedim(-1, 0)

Please let me know if you have any problems.

[jiepainanhai]

It works very well, thank you

[ar4]

That is good news. I will then close this Issue, but please feel free to reopen it if you encounter any problems.