DL-Art-School/codes/utils/fdpl_util.py

import numpy as np
import torch
import torch.nn as nn

# note: all dct related functions are either exactly as or based on those
# at https://github.com/zh217/torch-dct
def dct(x, norm=None):
    """
    Discrete Cosine Transform, Type II (a.k.a. the DCT)
    For the meaning of the parameter `norm`, see:
    https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
    :param x: the input signal
    :param norm: the normalization, None or 'ortho'
    :return: the DCT-II of the signal over the last dimension
    """
    x_shape = x.shape
    N = x_shape[-1]
    x = x.contiguous().view(-1, N)

    v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)

    Vc = torch.rfft(v, 1, onesided=False)

    k = - torch.arange(N, dtype=x.dtype, device=x.device)[None, :] * np.pi / (2 * N)
    W_r = torch.cos(k)
    W_i = torch.sin(k)

    V = Vc[:, :, 0] * W_r - Vc[:, :, 1] * W_i

    if norm == 'ortho':
        V[:, 0] /= np.sqrt(N) * 2
        V[:, 1:] /= np.sqrt(N / 2) * 2

    V = 2 * V.view(*x_shape)

    return V

def idct(X, norm=None):
    """
    The inverse to DCT-II, which is a scaled Discrete Cosine Transform, Type III
    Our definition of idct is that idct(dct(x)) == x
    For the meaning of the parameter `norm`, see:
    https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
    :param X: the input signal
    :param norm: the normalization, None or 'ortho'
    :return: the inverse DCT-II of the signal over the last dimension
    """

    x_shape = X.shape
    N = x_shape[-1]

    X_v = X.contiguous().view(-1, x_shape[-1]) / 2

    if norm == 'ortho':
        X_v[:, 0] *= np.sqrt(N) * 2
        X_v[:, 1:] *= np.sqrt(N / 2) * 2

    k = torch.arange(x_shape[-1], dtype=X.dtype, device=X.device)[None, :] * np.pi / (2 * N)
    W_r = torch.cos(k)
    W_i = torch.sin(k)

    V_t_r = X_v
    V_t_r = V_t_r.to(device)
    V_t_i = torch.cat([X_v[:, :1] * 0, -X_v.flip([1])[:, :-1]], dim=1)
    V_t_i = V_t_i.to(device)

    V_r = V_t_r * W_r - V_t_i * W_i
    V_i = V_t_r * W_i + V_t_i * W_r

    V = torch.cat([V_r.unsqueeze(2), V_i.unsqueeze(2)], dim=2)

    v = torch.irfft(V, 1, onesided=False)
    x = v.new_zeros(v.shape)
    x[:, ::2] += v[:, :N - (N // 2)]
    x[:, 1::2] += v.flip([1])[:, :N // 2]

    return x.view(*x_shape)

def dct_2d(x, norm=None):
    """
    2-dimensional Discrete Cosine Transform, Type II (a.k.a. the DCT)
    For the meaning of the parameter `norm`, see:
    https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
    :param x: the input signal
    :param norm: the normalization, None or 'ortho'
    :return: the DCT-II of the signal over the last 2 dimensions
    """
    X1 = dct(x, norm=norm)
    X2 = dct(X1.transpose(-1, -2), norm=norm)
    return X2.transpose(-1, -2)

def idct_2d(X, norm=None):
    """
    The inverse to 2D DCT-II, which is a scaled Discrete Cosine Transform, Type III
    Our definition of idct is that idct_2d(dct_2d(x)) == x
    For the meaning of the parameter `norm`, see:
    https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
    :param X: the input signal
    :param norm: the normalization, None or 'ortho'
    :return: the DCT-II of the signal over the last 2 dimensions
    """
    x1 = idct(X, norm=norm)
    x2 = idct(x1.transpose(-1, -2), norm=norm)
    return x2.transpose(-1, -2)

def extract_patches_2d(img,patch_shape,step=[1.0,1.0],batch_first=False):
    """
        source: https://gist.github.com/dem123456789/23f18fd78ac8da9615c347905e64fc78
    """
    patch_H, patch_W = patch_shape[0], patch_shape[1]
    if(img.size(2) < patch_H):
        num_padded_H_Top = (patch_H - img.size(2))//2
        num_padded_H_Bottom = patch_H - img.size(2) - num_padded_H_Top
        padding_H = nn.ConstantPad2d((0, 0, num_padded_H_Top, num_padded_H_Bottom), 0)
        img = padding_H(img)
    if(img.size(3) < patch_W):
        num_padded_W_Left = (patch_W - img.size(3))//2
        num_padded_W_Right = patch_W - img.size(3) - num_padded_W_Left
        padding_W = nn.ConstantPad2d((num_padded_W_Left,num_padded_W_Right, 0, 0), 0)
        img = padding_W(img)
    step_int = [0, 0]
    step_int[0] = int(patch_H*step[0]) if(isinstance(step[0], float)) else step[0]
    step_int[1] = int(patch_W*step[1]) if(isinstance(step[1], float)) else step[1]
    patches_fold_H = img.unfold(2, patch_H, step_int[0])
    if((img.size(2) - patch_H) % step_int[0] != 0):
        patches_fold_H = torch.cat((patches_fold_H,
                                    img[:, :, -patch_H:, :].permute(0,1,3,2).unsqueeze(2)),dim=2)
    patches_fold_HW = patches_fold_H.unfold(3, patch_W, step_int[1])
    if((img.size(3) - patch_W) % step_int[1] != 0):
        patches_fold_HW = torch.cat((patches_fold_HW,
                                     patches_fold_H[:, :, :, -patch_W:, :].permute(0, 1, 2, 4, 3).unsqueeze(3)), dim=3)
    patches = patches_fold_HW.permute(2, 3, 0, 1, 4, 5)
    patches = patches.reshape(-1, img.size(0), img.size(1), patch_H, patch_W)
    if(batch_first):
        patches = patches.permute(1, 0, 2, 3, 4)
    return patches