121 lines
4.9 KiB
Python
121 lines
4.9 KiB
Python
import torch
|
|
from torch import nn as nn
|
|
|
|
import models.srflow.Permutations
|
|
import models.srflow.FlowAffineCouplingsAblation
|
|
import models.srflow.FlowActNorms
|
|
|
|
|
|
def getConditional(rrdbResults, position):
|
|
img_ft = rrdbResults if isinstance(rrdbResults, torch.Tensor) else rrdbResults[position]
|
|
return img_ft
|
|
|
|
|
|
class FlowStep(nn.Module):
|
|
FlowPermutation = {
|
|
"reverse": lambda obj, z, logdet, rev: (obj.reverse(z, rev), logdet),
|
|
"shuffle": lambda obj, z, logdet, rev: (obj.shuffle(z, rev), logdet),
|
|
"invconv": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
"squeeze_invconv": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
"resqueeze_invconv_alternating_2_3": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
"resqueeze_invconv_3": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
"InvertibleConv1x1GridAlign": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
"InvertibleConv1x1SubblocksShuf": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
"InvertibleConv1x1GridAlignIndepBorder": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
"InvertibleConv1x1GridAlignIndepBorder4": lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev),
|
|
}
|
|
|
|
def __init__(self, in_channels, hidden_channels,
|
|
actnorm_scale=1.0, flow_permutation="invconv", flow_coupling="additive",
|
|
LU_decomposed=False, opt=None, image_injector=None, idx=None, acOpt=None, normOpt=None, in_shape=None,
|
|
position=None):
|
|
# check configures
|
|
assert flow_permutation in FlowStep.FlowPermutation, \
|
|
"float_permutation should be in `{}`".format(
|
|
FlowStep.FlowPermutation.keys())
|
|
super().__init__()
|
|
self.flow_permutation = flow_permutation
|
|
self.flow_coupling = flow_coupling
|
|
self.image_injector = image_injector
|
|
|
|
self.norm_type = normOpt['type'] if normOpt else 'ActNorm2d'
|
|
self.position = normOpt['position'] if normOpt else None
|
|
|
|
self.in_shape = in_shape
|
|
self.position = position
|
|
self.acOpt = acOpt
|
|
|
|
# 1. actnorm
|
|
self.actnorm = models.srflow.FlowActNorms.ActNorm2d(in_channels, actnorm_scale)
|
|
|
|
# 2. permute
|
|
if flow_permutation == "invconv":
|
|
self.invconv = models.srflow.Permutations.InvertibleConv1x1(
|
|
in_channels, LU_decomposed=LU_decomposed)
|
|
|
|
# 3. coupling
|
|
if flow_coupling == "CondAffineSeparatedAndCond":
|
|
self.affine = models.srflow.FlowAffineCouplingsAblation.CondAffineSeparatedAndCond(in_channels=in_channels,
|
|
opt=opt)
|
|
elif flow_coupling == "noCoupling":
|
|
pass
|
|
else:
|
|
raise RuntimeError("coupling not Found:", flow_coupling)
|
|
|
|
def forward(self, input, logdet=None, rrdbResults=None, reverse=False):
|
|
if not reverse:
|
|
return self.normal_flow(input, logdet, rrdbResults)
|
|
else:
|
|
return self.reverse_flow(input, logdet, rrdbResults)
|
|
|
|
def normal_flow(self, z, logdet, rrdbResults=None):
|
|
if self.flow_coupling == "bentIdentityPreAct":
|
|
z, logdet = self.bentIdentPar(z, logdet, reverse=False)
|
|
|
|
# 1. actnorm
|
|
if self.norm_type == "ConditionalActNormImageInjector":
|
|
img_ft = getConditional(rrdbResults, self.position)
|
|
z, logdet = self.actnorm(z, img_ft=img_ft, logdet=logdet, reverse=False)
|
|
elif self.norm_type == "noNorm":
|
|
pass
|
|
else:
|
|
z, logdet = self.actnorm(z, logdet=logdet, reverse=False)
|
|
|
|
# 2. permute
|
|
z, logdet = FlowStep.FlowPermutation[self.flow_permutation](
|
|
self, z, logdet, False)
|
|
|
|
need_features = self.affine_need_features()
|
|
|
|
# 3. coupling
|
|
if need_features or self.flow_coupling in ["condAffine", "condFtAffine", "condNormAffine"]:
|
|
img_ft = getConditional(rrdbResults, self.position)
|
|
z, logdet = self.affine(input=z, logdet=logdet, reverse=False, ft=img_ft)
|
|
return z, logdet
|
|
|
|
def reverse_flow(self, z, logdet, rrdbResults=None):
|
|
|
|
need_features = self.affine_need_features()
|
|
|
|
# 1.coupling
|
|
if need_features or self.flow_coupling in ["condAffine", "condFtAffine", "condNormAffine"]:
|
|
img_ft = getConditional(rrdbResults, self.position)
|
|
z, logdet = self.affine(input=z, logdet=logdet, reverse=True, ft=img_ft)
|
|
|
|
# 2. permute
|
|
z, logdet = FlowStep.FlowPermutation[self.flow_permutation](
|
|
self, z, logdet, True)
|
|
|
|
# 3. actnorm
|
|
z, logdet = self.actnorm(z, logdet=logdet, reverse=True)
|
|
|
|
return z, logdet
|
|
|
|
def affine_need_features(self):
|
|
need_features = False
|
|
try:
|
|
need_features = self.affine.need_features
|
|
except:
|
|
pass
|
|
return need_features
|