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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import split_feature, compute_same_pad
def gaussian_p(mean, logs, x):
"""
lnL = -1/2 * { ln|Var| + ((X - Mu)^T)(Var^-1)(X - Mu) + kln(2*PI) }
k = 1 (Independent)
Var = logs ** 2
"""
c = math.log(2 * math.pi)
return -0.5 * (logs * 2.0 + ((x - mean) ** 2) / torch.exp(logs * 2.0) + c)
def gaussian_likelihood(mean, logs, x):
p = gaussian_p(mean, logs, x)
return torch.sum(p, dim=[1, 2, 3])
def gaussian_sample(mean, logs, temperature=1):
# Sample from Gaussian with temperature
z = torch.normal(mean, torch.exp(logs) * temperature)
return z
def squeeze2d(input, factor):
if factor == 1:
return input
B, C, H, W = input.size()
assert H % factor == 0 and W % factor == 0, "H or W modulo factor is not 0"
x = input.view(B, C, H // factor, factor, W // factor, factor)
x = x.permute(0, 1, 3, 5, 2, 4).contiguous()
x = x.view(B, C * factor * factor, H // factor, W // factor)
return x
def unsqueeze2d(input, factor):
if factor == 1:
return input
factor2 = factor**2
B, C, H, W = input.size()
assert C % (factor2) == 0, "C module factor squared is not 0"
x = input.view(B, C // factor2, factor, factor, H, W)
x = x.permute(0, 1, 4, 2, 5, 3).contiguous()
x = x.view(B, C // (factor2), H * factor, W * factor)
return x
class _ActNorm(nn.Module):
"""
Activation Normalization
Initialize the bias and scale with a given minibatch,
so that the output per-channel have zero mean and unit variance for that.
After initialization, `bias` and `logs` will be trained as parameters.
"""
def __init__(self, num_features, scale=1.0):
super().__init__()
# register mean and scale
size = [1, num_features, 1, 1]
self.bias = nn.Parameter(torch.zeros(*size))
self.logs = nn.Parameter(torch.zeros(*size))
self.num_features = num_features
self.scale = scale
self.inited = False
def initialize_parameters(self, input):
if not self.training:
raise ValueError("In Eval mode, but ActNorm not inited")
with torch.no_grad():
bias = -torch.mean(input.clone(), dim=[0, 2, 3], keepdim=True)
vars = torch.mean((input.clone() + bias) ** 2, dim=[0, 2, 3], keepdim=True)
logs = torch.log(self.scale / (torch.sqrt(vars) + 1e-6))
self.bias.data.copy_(bias.data)
self.logs.data.copy_(logs.data)
self.inited = True
def _center(self, input, reverse=False):
if reverse:
return input - self.bias
else:
return input + self.bias
def _scale(self, input, logdet=None, reverse=False):
if reverse:
input = input * torch.exp(-self.logs)
else:
input = input * torch.exp(self.logs)
if logdet is not None:
"""
logs is log_std of `mean of channels`
so we need to multiply by number of pixels
"""
b, c, h, w = input.shape
dlogdet = torch.sum(self.logs) * h * w
if reverse:
dlogdet *= -1
logdet = logdet + dlogdet
return input, logdet
def forward(self, input, logdet=None, reverse=False):
self._check_input_dim(input)
if not self.inited:
self.initialize_parameters(input)
if reverse:
input, logdet = self._scale(input, logdet, reverse)
input = self._center(input, reverse)
else:
input = self._center(input, reverse)
input, logdet = self._scale(input, logdet, reverse)
return input, logdet
class ActNorm2d(_ActNorm):
def __init__(self, num_features, scale=1.0):
super().__init__(num_features, scale)
def _check_input_dim(self, input):
assert len(input.size()) == 4
assert input.size(1) == self.num_features, (
"[ActNorm]: input should be in shape as `BCHW`,"
" channels should be {} rather than {}".format(
self.num_features, input.size()
)
)
class LinearZeros(nn.Module):
def __init__(self, in_channels, out_channels, logscale_factor=3):
super().__init__()
self.linear = nn.Linear(in_channels, out_channels)
self.linear.weight.data.zero_()
self.linear.bias.data.zero_()
self.logscale_factor = logscale_factor
self.logs = nn.Parameter(torch.zeros(out_channels))
def forward(self, input):
output = self.linear(input)
return output * torch.exp(self.logs * self.logscale_factor)
class Conv2d(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding="same",
do_actnorm=True,
weight_std=0.05,
):
super().__init__()
if padding == "same":
padding = compute_same_pad(kernel_size, stride)
elif padding == "valid":
padding = 0
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride,
padding,
bias=(not do_actnorm),
)
# init weight with std
self.conv.weight.data.normal_(mean=0.0, std=weight_std)
if not do_actnorm:
self.conv.bias.data.zero_()
else:
self.actnorm = ActNorm2d(out_channels)
self.do_actnorm = do_actnorm
def forward(self, input):
x = self.conv(input)
if self.do_actnorm:
x, _ = self.actnorm(x)
return x
class Conv2dZeros(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding="same",
logscale_factor=3,
):
super().__init__()
if padding == "same":
padding = compute_same_pad(kernel_size, stride)
elif padding == "valid":
padding = 0
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
self.conv.weight.data.zero_()
self.conv.bias.data.zero_()
self.logscale_factor = logscale_factor
self.logs = nn.Parameter(torch.zeros(out_channels, 1, 1))
def forward(self, input):
output = self.conv(input)
return output * torch.exp(self.logs * self.logscale_factor)
class Permute2d(nn.Module):
def __init__(self, num_channels, shuffle):
super().__init__()
self.num_channels = num_channels
self.indices = torch.arange(self.num_channels - 1, -1, -1, dtype=torch.long)
self.indices_inverse = torch.zeros((self.num_channels), dtype=torch.long)
for i in range(self.num_channels):
self.indices_inverse[self.indices[i]] = i
if shuffle:
self.reset_indices()
def reset_indices(self):
shuffle_idx = torch.randperm(self.indices.shape[0])
self.indices = self.indices[shuffle_idx]
for i in range(self.num_channels):
self.indices_inverse[self.indices[i]] = i
def forward(self, input, reverse=False):
assert len(input.size()) == 4
if not reverse:
input = input[:, self.indices, :, :]
return input
else:
return input[:, self.indices_inverse, :, :]
class Split2d(nn.Module):
def __init__(self, num_channels):
super().__init__()
self.conv = Conv2dZeros(num_channels // 2, num_channels)
def split2d_prior(self, z):
h = self.conv(z)
return split_feature(h, "cross")
def forward(self, input, logdet=0.0, reverse=False, temperature=None):
if reverse:
z1 = input
mean, logs = self.split2d_prior(z1)
z2 = gaussian_sample(mean, logs, temperature)
z = torch.cat((z1, z2), dim=1)
return z, logdet
else:
z1, z2 = split_feature(input, "split")
mean, logs = self.split2d_prior(z1)
logdet = gaussian_likelihood(mean, logs, z2) + logdet
return z1, logdet
class SqueezeLayer(nn.Module):
def __init__(self, factor):
super().__init__()
self.factor = factor
def forward(self, input, logdet=None, reverse=False):
if reverse:
output = unsqueeze2d(input, self.factor)
else:
output = squeeze2d(input, self.factor)
return output, logdet
class InvertibleConv1x1(nn.Module):
def __init__(self, num_channels, LU_decomposed):
super().__init__()
w_shape = [num_channels, num_channels]
w_init = torch.linalg.qr(torch.randn(*w_shape))[0]
if not LU_decomposed:
self.weight = nn.Parameter(torch.Tensor(w_init))
else:
p, lower, upper = torch.lu_unpack(*torch.lu(w_init))
s = torch.diag(upper)
sign_s = torch.sign(s)
log_s = torch.log(torch.abs(s))
upper = torch.triu(upper, 1)
l_mask = torch.tril(torch.ones(w_shape), -1)
eye = torch.eye(*w_shape)
self.register_buffer("p", p)
self.register_buffer("sign_s", sign_s)
self.lower = nn.Parameter(lower)
self.log_s = nn.Parameter(log_s)
self.upper = nn.Parameter(upper)
self.l_mask = l_mask
self.eye = eye
self.w_shape = w_shape
self.LU_decomposed = LU_decomposed
def get_weight(self, input, reverse):
b, c, h, w = input.shape
if not self.LU_decomposed:
dlogdet = torch.slogdet(self.weight)[1] * h * w
if reverse:
weight = torch.inverse(self.weight)
else:
weight = self.weight
else:
self.l_mask = self.l_mask.to(input.device)
self.eye = self.eye.to(input.device)
lower = self.lower * self.l_mask + self.eye
u = self.upper * self.l_mask.transpose(0, 1).contiguous()
u += torch.diag(self.sign_s * torch.exp(self.log_s))
dlogdet = torch.sum(self.log_s) * h * w
if reverse:
u_inv = torch.inverse(u)
l_inv = torch.inverse(lower)
p_inv = torch.inverse(self.p)
weight = torch.matmul(u_inv, torch.matmul(l_inv, p_inv))
else:
weight = torch.matmul(self.p, torch.matmul(lower, u))
return weight.view(self.w_shape[0], self.w_shape[1], 1, 1), dlogdet
def forward(self, input, logdet=None, reverse=False):
"""
log-det = log|abs(|W|)| * pixels
"""
weight, dlogdet = self.get_weight(input, reverse)
if not reverse:
z = F.conv2d(input, weight)
if logdet is not None:
logdet = logdet + dlogdet
return z, logdet
else:
z = F.conv2d(input, weight)
if logdet is not None:
logdet = logdet - dlogdet
return z, logdet
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