DL-Art-School/codes/models/archs/srflow_orig/FlowAffineCouplingsAblation.py

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import torch
from torch import nn as nn
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from models.archs.srflow_orig import thops
from models.archs.srflow_orig.flow import Conv2d, Conv2dZeros
from utils.util import opt_get
class CondAffineSeparatedAndCond(nn.Module):
def __init__(self, in_channels, opt):
super().__init__()
self.need_features = True
self.in_channels = in_channels
self.in_channels_rrdb = 320
self.kernel_hidden = 1
self.affine_eps = 0.0001
self.n_hidden_layers = 1
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hidden_channels = opt_get(opt, ['networks', 'generator','flow', 'CondAffineSeparatedAndCond', 'hidden_channels'])
self.hidden_channels = 64 if hidden_channels is None else hidden_channels
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self.affine_eps = opt_get(opt, ['networks', 'generator','flow', 'CondAffineSeparatedAndCond', 'eps'], 0.0001)
self.channels_for_nn = self.in_channels // 2
self.channels_for_co = self.in_channels - self.channels_for_nn
if self.channels_for_nn is None:
self.channels_for_nn = self.in_channels // 2
self.fAffine = self.F(in_channels=self.channels_for_nn + self.in_channels_rrdb,
out_channels=self.channels_for_co * 2,
hidden_channels=self.hidden_channels,
kernel_hidden=self.kernel_hidden,
n_hidden_layers=self.n_hidden_layers)
self.fFeatures = self.F(in_channels=self.in_channels_rrdb,
out_channels=self.in_channels * 2,
hidden_channels=self.hidden_channels,
kernel_hidden=self.kernel_hidden,
n_hidden_layers=self.n_hidden_layers)
def forward(self, input: torch.Tensor, logdet=None, reverse=False, ft=None):
if not reverse:
z = input
assert z.shape[1] == self.in_channels, (z.shape[1], self.in_channels)
# Feature Conditional
scaleFt, shiftFt = self.feature_extract(ft, self.fFeatures)
z = z + shiftFt
z = z * scaleFt
logdet = logdet + self.get_logdet(scaleFt)
# Self Conditional
z1, z2 = self.split(z)
scale, shift = self.feature_extract_aff(z1, ft, self.fAffine)
self.asserts(scale, shift, z1, z2)
z2 = z2 + shift
z2 = z2 * scale
logdet = logdet + self.get_logdet(scale)
z = thops.cat_feature(z1, z2)
output = z
else:
z = input
# Self Conditional
z1, z2 = self.split(z)
scale, shift = self.feature_extract_aff(z1, ft, self.fAffine)
self.asserts(scale, shift, z1, z2)
z2 = z2 / scale
z2 = z2 - shift
z = thops.cat_feature(z1, z2)
logdet = logdet - self.get_logdet(scale)
# Feature Conditional
scaleFt, shiftFt = self.feature_extract(ft, self.fFeatures)
z = z / scaleFt
z = z - shiftFt
logdet = logdet - self.get_logdet(scaleFt)
output = z
return output, logdet
def asserts(self, scale, shift, z1, z2):
assert z1.shape[1] == self.channels_for_nn, (z1.shape[1], self.channels_for_nn)
assert z2.shape[1] == self.channels_for_co, (z2.shape[1], self.channels_for_co)
assert scale.shape[1] == shift.shape[1], (scale.shape[1], shift.shape[1])
assert scale.shape[1] == z2.shape[1], (scale.shape[1], z1.shape[1], z2.shape[1])
def get_logdet(self, scale):
return thops.sum(torch.log(scale), dim=[1, 2, 3])
def feature_extract(self, z, f):
h = f(z)
shift, scale = thops.split_feature(h, "cross")
scale = (torch.sigmoid(scale + 2.) + self.affine_eps)
return scale, shift
def feature_extract_aff(self, z1, ft, f):
z = torch.cat([z1, ft], dim=1)
h = f(z)
shift, scale = thops.split_feature(h, "cross")
scale = (torch.sigmoid(scale + 2.) + self.affine_eps)
return scale, shift
def split(self, z):
z1 = z[:, :self.channels_for_nn]
z2 = z[:, self.channels_for_nn:]
assert z1.shape[1] + z2.shape[1] == z.shape[1], (z1.shape[1], z2.shape[1], z.shape[1])
return z1, z2
def F(self, in_channels, out_channels, hidden_channels, kernel_hidden=1, n_hidden_layers=1):
layers = [Conv2d(in_channels, hidden_channels), nn.ReLU(inplace=False)]
for _ in range(n_hidden_layers):
layers.append(Conv2d(hidden_channels, hidden_channels, kernel_size=[kernel_hidden, kernel_hidden]))
layers.append(nn.ReLU(inplace=False))
layers.append(Conv2dZeros(hidden_channels, out_channels))
return nn.Sequential(*layers)