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util.py
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util.py
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import math
import unittest
import pytorch_wavelets as pwt
import torch as th
import torch.nn.functional as F
import torch.nn.functional as tf
import models
import shltutils.filters as filters
def get_models(sz=320):
M, N = sz, sz
n_w = 63 * 2 - 1
kernel_size = 7
n_f = kernel_size**2 - 1
R_gmm = models.ProductGMM(
n_f=n_f,
bound_norm=False,
zero_mean=True,
symmetric=True,
ortho=True,
vmin=-1,
vmax=1,
kernel_size=kernel_size,
K_init='random',
n_w=n_w,
w_init='student-t',
).cuda()
R_gmm.load_state_dict(th.load('./out/patch/state_final.pth'))
n_scales = 20
R_gsm = models.ProductGSM(
n_f=n_f,
bound_norm=False,
zero_mean=True,
ortho=True,
n_scales=n_scales,
kernel_size=kernel_size,
K_init='random',
).cuda()
R_gsm.load_state_dict(th.load('./out/gsm/state_final.pth'))
kernel_size = 15
n_f = kernel_size**2 - 1
R_gmm15 = models.ProductGMM(
n_f=n_f,
bound_norm=False,
zero_mean=True,
symmetric=False,
ortho=True,
vmin=-1,
vmax=1,
kernel_size=kernel_size,
K_init='random',
n_w=n_w,
w_init='student-t',
).cuda()
R_gmm15.load_state_dict(th.load('./out/patch15/state_final.pth'))
n_w = 125
h = th.tensor([
0.0104933261758410, -0.0263483047033631, -0.0517766952966370,
0.276348304703363, 0.582566738241592, 0.276348304703363,
-0.0517766952966369, -0.0263483047033631, 0.0104933261758408
],
device='cuda')
lamda = th.ones((21, ), dtype=th.float32, device='cuda')
lamda = lamda.float()
h0, _ = filters.dfilters('dmaxflat4', 'd')
P = th.from_numpy(filters.modulate2(h0, 'c')).float()
R_sh = models.GMMConv(
n_scales=2,
symmetric=True,
vmin=-.5,
vmax=.5,
n_w=n_w,
w_init='abs',
lamda=lamda.cuda(),
h=h.cuda(),
dims=(sz, sz),
P=P.cuda()
).cuda()
R_sh.load_state_dict(th.load('./out/shearlets/state_final.pth'))
R_sh.set_eta(shape=(sz, sz))
R_sh.set_sigma(0)
n_w = 125
Rs_wavelet = []
for wave in ['db2', 'db4']:
levels = 2
K = pwt.DWTForward(wave=wave, J=levels,
mode='reflect').cuda().to(th.float64)
mus_each = []
xs_pot = []
data = th.load('./celeba-resized.pth').cuda().to(th.float64)[:5000]
n_points = 20
for level in range(levels):
for direction in range(3):
qs = th.quantile(
K(data[:10_000])[1][level][:, :, direction], 0.999
) * 1.1
# qs = .7
mus_each.append(th.linspace(-qs, qs, n_w, device='cuda')[None])
xs_pot.append(
th.linspace(
-qs, qs, n_points**2, device='cuda', dtype=th.float64
)
)
mus = th.cat(mus_each, dim=0)
gamma = .5
R = models.WaveletGMM(
mus=mus,
levels=levels,
vmin=-gamma,
vmax=gamma,
n_w=n_w,
w_init='student-t',
im_sz=sz,
wave=wave,
).cuda().to(th.float64)
R.set_sigma(0)
R.load_state_dict(th.load(f'./out/wavelets/{wave}/state_final.pth'))
Rs_wavelet.append(R.to(th.float32))
return R_gmm, R_gmm15, R_gsm, *Rs_wavelet, R_sh
def mahalanobis(x, mus, precs_chol):
distances = th.empty((mus.shape[0], x.shape[0]), device=x.device)
for k, (mu, L) in enumerate(zip(mus, precs_chol)):
distances[k] = (((x - mu) @ L)**2).sum(1)
return distances
def lognormal(x, mus, prec):
# print(th.linalg.slogdet(prec)[0])
prec_chol = th.linalg.cholesky(prec)
maha_dist = mahalanobis(x, mus, prec_chol)
logdets = th.linalg.slogdet(prec_chol)[1]
return -(math.log(2 * math.pi) * mus.shape[1] +
maha_dist) / 2 + logdets[:, None]
def patch2image(batches, img_dims, size=(2, 2), stride=(1, 1)):
tmp = batches[None].permute(0, 2, 1)
return th.squeeze(F.fold(tmp, img_dims, size, 1, 0, stride))
def image2patch(image, size=(2, 2), stride=(1, 1)):
return image.unfold(0, size[0], stride[0]).unfold(
1, size[1], stride[1]
).contiguous().view(-1, size[0] * size[1])
def proj_simplex(
x: th.Tensor, # 1-D vector of weights
s: float = 1., # axis intersection
):
k = th.linspace(1, len(x), len(x), device=x.device)
x_s = th.sort(x, dim=0, descending=True)[0]
t = (th.cumsum(x_s, dim=0) - s) / k
mu = th.max(t)
return th.clamp(x - mu, 0, s)
def proj_simplex_simul(
x: th.Tensor, # 2-D array of weights,
# projection is performed along the last axis axis
s: float = 1., # axis interesection
):
K = x.shape[1]
k = th.linspace(1, K, K, device=x.device)
x_s = th.sort(x, dim=1, descending=True)[0]
t = (th.cumsum(x_s, dim=1) - s) / k[None]
mu = th.max(t, dim=1, keepdim=True)[0]
return th.clamp(x - mu, 0, s)
def weight_init(
vmin: float,
vmax: float,
n_w: int,
scale: float,
mode: str,
) -> th.Tensor:
x = th.linspace(vmin, vmax, n_w, dtype=th.float32)
match mode:
case "constant":
w = th.ones_like(x) * scale
case "linear":
w = x * scale
case "quadratic":
w = x**2 * scale
case "abs":
w = th.abs(x) * scale
w -= w.max()
w = w.abs()
case "student-t":
alpha = 100
w = scale * math.sqrt(alpha) / (1 + 0.5 * alpha * x**2)
case "Student-T":
a_ = 0.1 * 78
b_ = 0.1 * 78**2
denom = 1 + (a_ * x)**2
w = b_ / (2 * a_**2) * th.log(denom)
return w
def _get_gauss_kernel() -> th.Tensor:
return th.tensor([[
[1.0, 4.0, 6.0, 4.0, 1.0],
[4.0, 16.0, 24.0, 16.0, 4.0],
[6.0, 24.0, 36.0, 24.0, 6.0],
[4.0, 16.0, 24.0, 16.0, 4.0],
[1.0, 4.0, 6.0, 4.0, 1.0],
]]) / 256.0
def _compute_padding(kernel_size: list[int]) -> list[int]:
computed = [k // 2 for k in kernel_size]
out_padding = 2 * len(kernel_size) * [0]
for i in range(len(kernel_size)):
computed_tmp = computed[-(i + 1)]
if kernel_size[i] % 2 == 0:
padding = computed_tmp - 1
else:
padding = computed_tmp
out_padding[2 * i + 0] = padding
out_padding[2 * i + 1] = computed_tmp
return out_padding
def psnr(x, y):
return th.mean(20 * th.log10(1 / th.sqrt(th.mean((x - y)**2,
(1, 2, 3))))).item()
def init_params(mode, vmin, vmax, n_f, n_w, symmetric):
x = th.linspace(vmin, vmax, n_w, dtype=th.float32)
match mode:
case "constant":
w = th.ones_like(x)
case "linear":
w = x
case "quadratic":
w = -x**2
w -= w.min()
case "abs":
w = -th.abs(x)
w -= w.min()
case "student-t":
alpha = 1000
w = math.sqrt(alpha) / (1 + 0.5 * alpha * x**2)
case "Student-T":
a_ = 0.1 * 78
b_ = 0.1 * 78**2
denom = 1 + (a_ * x)**2
w = b_ / (2 * a_**2) * th.log(denom)
case 'random':
w = th.rand((n_w, ))
w /= w.sum()
if symmetric:
w = w[w.shape[0] // 2:][None].repeat(n_f, 1).clone()
else:
w = th.nn.Parameter(w[None].repeat(n_f, 1).clone())
return w
class SSIM(th.nn.Module):
def __init__(self, win_size: int = 7, k1: float = 0.01, k2: float = 0.03):
"""
Args:
win_size: Window size for SSIM calculation.
k1: k1 parameter for SSIM calculation.
k2: k2 parameter for SSIM calculation.
"""
super().__init__()
self.win_size = win_size
self.k1, self.k2 = k1, k2
self.register_buffer(
"w",
th.ones(1, 1, win_size, win_size) / win_size**2
)
NP = win_size**2
self.cov_norm = NP / (NP - 1)
self.w: th.Tensor
def forward(
self,
X: th.Tensor,
Y: th.Tensor,
data_range: th.Tensor | float = 1.,
reduced: bool = True,
):
C1 = (self.k1 * data_range)**2
C2 = (self.k2 * data_range)**2
ux = tf.conv2d(X, self.w)
uy = tf.conv2d(Y, self.w)
uxx = tf.conv2d(X * X, self.w)
uyy = tf.conv2d(Y * Y, self.w)
uxy = tf.conv2d(X * Y, self.w)
vx = self.cov_norm * (uxx - ux * ux)
vy = self.cov_norm * (uyy - uy * uy)
vxy = self.cov_norm * (uxy - ux * uy)
A1, A2, B1, B2 = (
2 * ux * uy + C1,
2 * vxy + C2,
ux**2 + uy**2 + C1,
vx + vy + C2,
)
D = B1 * B2
S = (A1 * A2) / D
if reduced:
return S.mean()
else:
return S