convolution.py

# coding=utf-8

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import abc
from typing import List, Tuple, Union, Optional
from warnings import warn

import math

import torch
import torch.nn as nn

from functools import partial

from torch_harmonics.quadrature import _precompute_grid, _precompute_latitudes
from torch_harmonics._disco_convolution import _disco_s2_contraction_torch, _disco_s2_transpose_contraction_torch
from torch_harmonics._disco_convolution import _disco_s2_contraction_cuda, _disco_s2_transpose_contraction_cuda
from torch_harmonics.filter_basis import get_filter_basis

# import custom C++/CUDA extensions if available
try:
    from disco_helpers import preprocess_psi
    import disco_cuda_extension

    _cuda_extension_available = True
except ImportError as err:
    disco_cuda_extension = None
    _cuda_extension_available = False


def _normalize_convolution_tensor_s2(psi_idx, psi_vals, in_shape, out_shape, kernel_size, quad_weights, transpose_normalization=False, merge_quadrature=False, eps=1e-9):
    """
    Discretely normalizes the convolution tensor.
    """

    nlat_in, nlon_in = in_shape
    nlat_out, nlon_out = out_shape

    # reshape the indices implicitly to be ikernel, lat_out, lat_in, lon_in
    idx = torch.stack([psi_idx[0], psi_idx[1], psi_idx[2] // nlon_in, psi_idx[2] % nlon_in], dim=0)

    if transpose_normalization:
        # pre-compute the quadrature weights
        q = quad_weights[idx[1]].reshape(-1)

        # loop through dimensions which require normalization
        for ik in range(kernel_size):
            for ilat in range(nlat_in):
                # get relevant entries
                iidx = torch.argwhere((idx[0] == ik) & (idx[2] == ilat))
                # normalize, while summing also over the input longitude dimension here as this is not available for the output
                vnorm = torch.sum(psi_vals[iidx] * q[iidx])
                if merge_quadrature:
                    # the correction factor accounts for the difference in longitudinal grid points when the input vector is upscaled
                    psi_vals[iidx] = psi_vals[iidx] * q[iidx] * nlon_in / nlon_out / (vnorm + eps)
                else:
                    psi_vals[iidx] = psi_vals[iidx] / (vnorm + eps)
    else:
        # pre-compute the quadrature weights
        q = quad_weights[idx[2]].reshape(-1)

        # loop through dimensions which require normalization
        for ik in range(kernel_size):
            for ilat in range(nlat_out):
                # get relevant entries
                iidx = torch.argwhere((idx[0] == ik) & (idx[1] == ilat))
                # normalize
                vnorm = torch.sum(psi_vals[iidx] * q[iidx])
                if merge_quadrature:
                    psi_vals[iidx] = psi_vals[iidx] * q[iidx] / (vnorm + eps)
                else:
                    psi_vals[iidx] = psi_vals[iidx] / (vnorm + eps)

    return psi_vals


def _precompute_convolution_tensor_s2(
    in_shape,
    out_shape,
    filter_basis,
    grid_in="equiangular",
    grid_out="equiangular",
    theta_cutoff=0.01 * math.pi,
    transpose_normalization=False,
    merge_quadrature=False,
):
    """
    Precomputes the rotated filters at positions $R^{-1}_j \omega_i = R^{-1}_j R_i \nu = Y(-\theta_j)Z(\phi_i - \phi_j)Y(\theta_j)\nu$.
    Assumes a tensorized grid on the sphere with an equidistant sampling in longitude as described in Ocampo et al.
    The output tensor has shape kernel_shape x nlat_out x (nlat_in * nlon_in).

    The rotation of the Euler angles uses the YZY convention, which applied to the northpole $(0,0,1)^T$ yields
    $$
    Y(\alpha) Z(\beta) Y(\gamma) n =
        {\begin{bmatrix}
            \cos(\gamma)\sin(\alpha) + \cos(\alpha)\cos(\beta)\sin(\gamma) \\
            \sin(\beta)\sin(\gamma) \\
            \cos(\alpha)\cos(\gamma)-\cos(\beta)\sin(\alpha)\sin(\gamma)
        \end{bmatrix}}
    $$
    """

    assert len(in_shape) == 2
    assert len(out_shape) == 2

    kernel_size = filter_basis.kernel_size

    nlat_in, nlon_in = in_shape
    nlat_out, nlon_out = out_shape

    lats_in, win = _precompute_latitudes(nlat_in, grid=grid_in)
    lats_in = torch.from_numpy(lats_in).float()
    lats_out, wout = _precompute_latitudes(nlat_out, grid=grid_out)
    lats_out = torch.from_numpy(lats_out).float()

    # compute the phi differences
    # It's imporatant to not include the 2 pi point in the longitudes, as it is equivalent to lon=0
    lons_in = torch.linspace(0, 2 * math.pi, nlon_in + 1)[:-1]

    out_idx = []
    out_vals = []
    for t in range(nlat_out):
        # the last angle has a negative sign as it is a passive rotation, which rotates the filter around the y-axis
        alpha = -lats_out[t]
        beta = lons_in
        gamma = lats_in.reshape(-1, 1)

        # compute cartesian coordinates of the rotated position
        # This uses the YZY convention of Euler angles, where the last angle (alpha) is a passive rotation,
        # and therefore applied with a negative sign
        z = -torch.cos(beta) * torch.sin(alpha) * torch.sin(gamma) + torch.cos(alpha) * torch.cos(gamma)
        x = torch.cos(alpha) * torch.cos(beta) * torch.sin(gamma) + torch.cos(gamma) * torch.sin(alpha)
        y = torch.sin(beta) * torch.sin(gamma)

        # normalization is emportant to avoid NaNs when arccos and atan are applied
        # this can otherwise lead to spurious artifacts in the solution
        norm = torch.sqrt(x * x + y * y + z * z)
        x = x / norm
        y = y / norm
        z = z / norm

        # compute spherical coordinates, where phi needs to fall into the [0, 2pi) range
        theta = torch.arccos(z)
        phi = torch.arctan2(y, x) + torch.pi

        # find the indices where the rotated position falls into the support of the kernel
        iidx, vals = filter_basis.compute_support_vals(theta, phi, r_cutoff=theta_cutoff)

        # add the output latitude and reshape such that psi has dimensions kernel_shape x nlat_out x (nlat_in*nlon_in)
        idx = torch.stack([iidx[:, 0], t * torch.ones_like(iidx[:, 0]), iidx[:, 1] * nlon_in + iidx[:, 2]], dim=0)

        # append indices and values to the COO datastructure
        out_idx.append(idx)
        out_vals.append(vals)

    # concatenate the indices and values
    out_idx = torch.cat(out_idx, dim=-1).to(torch.long).contiguous()
    out_vals = torch.cat(out_vals, dim=-1).to(torch.float32).contiguous()

    if transpose_normalization:
        quad_weights = 2.0 * torch.pi * torch.from_numpy(wout).float().reshape(-1, 1) / nlon_in
    else:
        quad_weights = 2.0 * torch.pi * torch.from_numpy(win).float().reshape(-1, 1) / nlon_in
    out_vals = _normalize_convolution_tensor_s2(
        out_idx, out_vals, in_shape, out_shape, kernel_size, quad_weights, transpose_normalization=transpose_normalization, merge_quadrature=merge_quadrature
    )

    return out_idx, out_vals


class DiscreteContinuousConv(nn.Module, metaclass=abc.ABCMeta):
    """
    Abstract base class for DISCO convolutions
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_shape: Union[int, List[int]],
        basis_type: Optional[str] = "piecewise linear",
        groups: Optional[int] = 1,
        bias: Optional[bool] = True,
    ):
        super().__init__()

        self.kernel_shape = kernel_shape

        # get the filter basis functions
        self.filter_basis = get_filter_basis(kernel_shape=kernel_shape, basis_type=basis_type)

        # groups
        self.groups = groups

        # weight tensor
        if in_channels % self.groups != 0:
            raise ValueError("Error, the number of input channels has to be an integer multiple of the group size")
        if out_channels % self.groups != 0:
            raise ValueError("Error, the number of output channels has to be an integer multiple of the group size")
        self.groupsize = in_channels // self.groups
        scale = math.sqrt(1.0 / self.groupsize / self.kernel_size)
        self.weight = nn.Parameter(scale * torch.randn(out_channels, self.groupsize, self.kernel_size))

        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channels))
        else:
            self.bias = None

    @property
    def kernel_size(self):
        return self.filter_basis.kernel_size

    @abc.abstractmethod
    def forward(self, x: torch.Tensor):
        raise NotImplementedError


class DiscreteContinuousConvS2(DiscreteContinuousConv):
    """
    Discrete-continuous convolutions (DISCO) on the 2-Sphere as described in [1].

    [1] Ocampo, Price, McEwen, Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions, ICLR (2023), arXiv:2209.13603
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        in_shape: Tuple[int],
        out_shape: Tuple[int],
        kernel_shape: Union[int, List[int]],
        basis_type: Optional[str] = "piecewise linear",
        groups: Optional[int] = 1,
        grid_in: Optional[str] = "equiangular",
        grid_out: Optional[str] = "equiangular",
        bias: Optional[bool] = True,
        theta_cutoff: Optional[float] = None,
    ):
        super().__init__(in_channels, out_channels, kernel_shape, basis_type, groups, bias)

        self.nlat_in, self.nlon_in = in_shape
        self.nlat_out, self.nlon_out = out_shape

        # heuristic to compute theta cutoff based on the bandlimit of the input field and overlaps of the basis functions
        if theta_cutoff is None:
            theta_cutoff = torch.pi / float(self.nlat_out - 1)

        if theta_cutoff <= 0.0:
            raise ValueError("Error, theta_cutoff has to be positive.")

        idx, vals = _precompute_convolution_tensor_s2(
            in_shape, out_shape, self.filter_basis, grid_in=grid_in, grid_out=grid_out, theta_cutoff=theta_cutoff, transpose_normalization=False, merge_quadrature=True
        )

        # sort the values
        ker_idx = idx[0, ...].contiguous()
        row_idx = idx[1, ...].contiguous()
        col_idx = idx[2, ...].contiguous()
        vals = vals.contiguous()

        if _cuda_extension_available:
            # preprocessed data-structure for GPU kernel
            roff_idx = preprocess_psi(self.kernel_size, out_shape[0], ker_idx, row_idx, col_idx, vals).contiguous()
            self.register_buffer("psi_roff_idx", roff_idx, persistent=False)

        # save all datastructures
        self.register_buffer("psi_ker_idx", ker_idx, persistent=False)
        self.register_buffer("psi_row_idx", row_idx, persistent=False)
        self.register_buffer("psi_col_idx", col_idx, persistent=False)
        self.register_buffer("psi_vals", vals, persistent=False)

    def extra_repr(self):
        r"""
        Pretty print module
        """
        return f"in_shape={(self.nlat_in, self.nlon_in)}, out_shape={(self.nlat_out, self.nlon_out)}, in_chans={self.groupsize * self.groups}, out_chans={self.weight.shape[0]}, filter_basis={self.filter_basis}, kernel_shape={self.kernel_shape}, groups={self.groups}"

    @property
    def psi_idx(self):
        return torch.stack([self.psi_ker_idx, self.psi_row_idx, self.psi_col_idx], dim=0).contiguous()

    def get_psi(self):
        psi = torch.sparse_coo_tensor(self.psi_idx, self.psi_vals, size=(self.kernel_size, self.nlat_out, self.nlat_in * self.nlon_in)).coalesce()
        return psi

    def forward(self, x: torch.Tensor) -> torch.Tensor:

        if x.is_cuda and _cuda_extension_available:
            x = _disco_s2_contraction_cuda(
                x, self.psi_roff_idx, self.psi_ker_idx, self.psi_row_idx, self.psi_col_idx, self.psi_vals, self.kernel_size, self.nlat_out, self.nlon_out
            )
        else:
            if x.is_cuda:
                warn("couldn't find CUDA extension, falling back to slow PyTorch implementation")
            psi = self.get_psi()
            x = _disco_s2_contraction_torch(x, psi, self.nlon_out)

        # extract shape
        B, C, K, H, W = x.shape
        x = x.reshape(B, self.groups, self.groupsize, K, H, W)

        # do weight multiplication
        out = torch.einsum("bgckxy,gock->bgoxy", x, self.weight.reshape(self.groups, -1, self.weight.shape[1], self.weight.shape[2])).contiguous()
        out = out.reshape(B, -1, H, W)

        if self.bias is not None:
            out = out + self.bias.reshape(1, -1, 1, 1)

        return out


class DiscreteContinuousConvTransposeS2(DiscreteContinuousConv):
    """
    Discrete-continuous transpose convolutions (DISCO) on the 2-Sphere as described in [1].

    [1] Ocampo, Price, McEwen, Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions, ICLR (2023), arXiv:2209.13603
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        in_shape: Tuple[int],
        out_shape: Tuple[int],
        kernel_shape: Union[int, List[int]],
        basis_type: Optional[str] = "piecewise linear",
        groups: Optional[int] = 1,
        grid_in: Optional[str] = "equiangular",
        grid_out: Optional[str] = "equiangular",
        bias: Optional[bool] = True,
        theta_cutoff: Optional[float] = None,
    ):
        super().__init__(in_channels, out_channels, kernel_shape, basis_type, groups, bias)

        self.nlat_in, self.nlon_in = in_shape
        self.nlat_out, self.nlon_out = out_shape

        # bandlimit
        if theta_cutoff is None:
            theta_cutoff = torch.pi / float(self.nlat_in - 1)

        if theta_cutoff <= 0.0:
            raise ValueError("Error, theta_cutoff has to be positive.")

        # switch in_shape and out_shape since we want transpose conv
        idx, vals = _precompute_convolution_tensor_s2(
            out_shape, in_shape, self.filter_basis, grid_in=grid_out, grid_out=grid_in, theta_cutoff=theta_cutoff, transpose_normalization=True, merge_quadrature=True
        )

        # sort the values
        ker_idx = idx[0, ...].contiguous()
        row_idx = idx[1, ...].contiguous()
        col_idx = idx[2, ...].contiguous()
        vals = vals.contiguous()

        if _cuda_extension_available:
            # preprocessed data-structure for GPU kernel
            roff_idx = preprocess_psi(self.kernel_size, in_shape[0], ker_idx, row_idx, col_idx, vals).contiguous()
            self.register_buffer("psi_roff_idx", roff_idx, persistent=False)

        # save all datastructures
        self.register_buffer("psi_ker_idx", ker_idx, persistent=False)
        self.register_buffer("psi_row_idx", row_idx, persistent=False)
        self.register_buffer("psi_col_idx", col_idx, persistent=False)
        self.register_buffer("psi_vals", vals, persistent=False)

    def extra_repr(self):
        r"""
        Pretty print module
        """
        return f"in_shape={(self.nlat_in, self.nlon_in)}, out_shape={(self.nlat_out, self.nlon_out)}, in_chans={self.groupsize * self.groups}, out_chans={self.weight.shape[0]}, filter_basis={self.filter_basis}, kernel_shape={self.kernel_shape}, groups={self.groups}"

    @property
    def psi_idx(self):
        return torch.stack([self.psi_ker_idx, self.psi_row_idx, self.psi_col_idx], dim=0).contiguous()

    def get_psi(self, semi_transposed: bool = False):
        if semi_transposed:
            # we do a semi-transposition to faciliate the computation
            tout = self.psi_idx[2] // self.nlon_out
            pout = self.psi_idx[2] % self.nlon_out
            # flip the axis of longitudes
            pout = self.nlon_out - 1 - pout
            tin = self.psi_idx[1]
            idx = torch.stack([self.psi_idx[0], tout, tin * self.nlon_out + pout], dim=0)
            psi = torch.sparse_coo_tensor(idx, self.psi_vals, size=(self.kernel_size, self.nlat_out, self.nlat_in * self.nlon_out)).coalesce()
        else:
            psi = torch.sparse_coo_tensor(self.psi_idx, self.psi_vals, size=(self.kernel_size, self.nlat_in, self.nlat_out * self.nlon_out)).coalesce()

        return psi

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # extract shape
        B, C, H, W = x.shape
        x = x.reshape(B, self.groups, self.groupsize, H, W)

        # do weight multiplication
        x = torch.einsum("bgcxy,gock->bgokxy", x, self.weight.reshape(self.groups, -1, self.weight.shape[1], self.weight.shape[2])).contiguous()
        x = x.reshape(B, -1, x.shape[-3], H, W)

        if x.is_cuda and _cuda_extension_available:
            out = _disco_s2_transpose_contraction_cuda(
                x, self.psi_roff_idx, self.psi_ker_idx, self.psi_row_idx, self.psi_col_idx, self.psi_vals, self.kernel_size, self.nlat_out, self.nlon_out
            )
        else:
            if x.is_cuda:
                warn("couldn't find CUDA extension, falling back to slow PyTorch implementation")
            psi = self.get_psi(semi_transposed=True)
            out = _disco_s2_transpose_contraction_torch(x, psi, self.nlon_out)

        if self.bias is not None:
            out = out + self.bias.reshape(1, -1, 1, 1)

        return out