atoms_detection/vae_image_utils.py

# @title Load functions for working with image coordinates and labels
# @title Load utility functions for data loading and preprocessing

from typing import Tuple, Union

import torch

import warnings
warnings.filterwarnings("ignore", module="torchvision.datasets")


def to_onehot(idx: torch.Tensor, n: int) -> torch.Tensor:
    """
    One-hot encoding of a label
    """
    if torch.max(idx).item() >= n:
        raise AssertionError(
            "Labelling must start from 0 and "
            "maximum label value must be less than total number of classes")
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    if idx.dim() == 1:
        idx = idx.unsqueeze(1)
    onehot = torch.zeros(idx.size(0), n, device=device)
    return onehot.scatter_(1, idx.to(device), 1)


def grid2xy(X1: torch.Tensor, X2: torch.Tensor) -> torch.Tensor:
    X = torch.cat((X1[None], X2[None]), 0)
    d0, d1 = X.shape[0], X.shape[1] * X.shape[2]
    X = X.reshape(d0, d1).T
    return X


def imcoordgrid(im_dim: Tuple) -> torch.Tensor:
    xx = torch.linspace(-1, 1, im_dim[0])
    yy = torch.linspace(1, -1, im_dim[1])
    x0, x1 = torch.meshgrid(xx, yy)
    return grid2xy(x0, x1)


def transform_coordinates(coord: torch.Tensor,
                          phi: Union[torch.Tensor, float] = 0,
                          coord_dx: Union[torch.Tensor, float] = 0,
                          ) -> torch.Tensor:

    if torch.sum(phi) == 0:
        phi = coord.new_zeros(coord.shape[0])
    rotmat_r1 = torch.stack([torch.cos(phi), torch.sin(phi)], 1)
    rotmat_r2 = torch.stack([-torch.sin(phi), torch.cos(phi)], 1)
    rotmat = torch.stack([rotmat_r1, rotmat_r2], axis=1)
    coord = torch.bmm(coord, rotmat)

    return coord + coord_dx