Delete utils.py

utils.py

@@ -1,118 +0,0 @@
-import torch
-import torch_dct as dct
-
-from torchvision.transforms import functional as F
-
-def flip_odd_lines(matrix):
-    """
-    Flip odd lines of a matrix
-    """
-    matrix = matrix.clone()
-
-    matrix[..., 1::2, :] = matrix[..., 1::2, :].flip(-1)
-
-    return matrix
-
-rotate = lambda m, r: torch.rot90(m, r, [-2, -1])
-sequency_vec = lambda m: flip_odd_lines(rotate(m, 0)).flatten(start_dim= m.dim()-2)
-sequency_mat = lambda v, s: rotate(flip_odd_lines(v.unflatten(-1, s)), 0)
-
-
-def modulo(x, L):
-    positive = x > 0
-    x = x % L
-    x = torch.where( ( x == 0) &  positive, L, x)
-    return x
-
-
-def center_modulo(x, L):
-    return modulo(x + L/2, L) - L/2
-
-
-def unmodulo(psi):
-
-    psi = torch.nn.functional.pad(psi, (1,1), mode='constant', value=0)
-    psi = torch.diff(psi, 1)
-    psi = dct.dct(psi, norm='ortho')
-    N = psi.shape[-1]
-    k = torch.arange(0, N)
-    denom = 2*( torch.cos(  torch.pi * k / N  )  -  1  )
-    denom[0] = 1.0
-    denom = denom.unsqueeze(0).unsqueeze(0) + 1e-7
-    psi     = psi / denom   
-    psi[..., 0] = 0.0
-    psi = dct.idct(psi, norm='ortho')
-    return psi
-
-RD = lambda x, L: torch.round( x / L)  * L
-
-
-def hard_thresholding(x, t):
-    return x * (torch.abs(x) > t)
-
-
-def stripe_estimation(psi, t=0.15):
-
-    dx = torch.diff(psi, 1, dim=-1)
-    dy = torch.diff(psi, 1, dim=-2)
-
-    dx = hard_thresholding(dx, t) 
-    dy = hard_thresholding(dy, t) 
-
-    dx = F.pad(dx, (1, 0, 1, 0))
-    dy = F.pad(dy, (0, 1, 0, 1))
-
-
-    rho = torch.diff(dx, 1, dim=-1) + torch.diff(dy, 1, dim=-2)
-    dct_rho = dct.dct_2d(rho, norm='ortho')
-
-
-    MX = rho.shape[-2]
-    NX = rho.shape[-1]
-
-    I, J = torch.meshgrid(torch.arange(0, MX), torch.arange(0, NX), indexing="ij")
-    I = I.to(rho.device)
-    J = J.to(rho.device)
-    denom = 2 * (torch.cos(torch.pi * I / MX ) + torch.cos(torch.pi * J / NX ) - 2)
-    denom = denom.unsqueeze(0).unsqueeze(0)
-    denom = denom.to(rho.device)
-    dct_phi = dct_rho / denom
-    dct_phi[..., 0, 0] = 0
-    phi = dct.idct_2d(dct_phi, norm='ortho')
-    phi = phi - torch.min(phi)
-    # phi = phi - torch.amin(phi, dim=(-1, -2), keepdim=True)
-    # phi = RD(phi, 1.0)
-    return phi
-
-
-def recons(m_t, DO=1, L=1.0, vertical=False, t=0.3):
-
-    if vertical:
-        m_t = m_t.permute(0, 1, 3, 2)
-
-    shape = m_t.shape[-2:]
-
-    modulo_vec = sequency_vec(m_t)
-    res = center_modulo( torch.diff(modulo_vec, n=DO), L) - torch.diff(modulo_vec, n=DO)
-    bl = res
-
-    for i in range(DO):
-        bl = unmodulo(bl)
-        bl = RD(bl, L)
-
-    x_est = bl
-    
-    x_est = sequency_mat(x_est, shape)   
-    x_est = x_est + m_t
-
-    if vertical:
-        x_est = x_est.permute(0, 1, 3, 2)
-
-    stripes = stripe_estimation(x_est, t=t)    
-    x_est = x_est - stripes
-
-    # if vertical:
-    #     x_est = x_est.permute(0, 1, 3, 2)
-    x_est = x_est - x_est.min()
-    x_est = x_est / x_est.max()
-    return x_est