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# Modified from:
# taming-transformers: https://github.com/CompVis/taming-transformers
import functools
import torch
import torch.nn as nn
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator as in Pix2Pix
--> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
"""
def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if not use_actnorm:
norm_layer = nn.BatchNorm2d
else:
norm_layer = ActNorm
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func != nn.BatchNorm2d
else:
use_bias = norm_layer != nn.BatchNorm2d
kw = 4
padw = 1
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
self.main = nn.Sequential(*sequence)
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
nn.init.normal_(module.weight.data, 0.0, 0.02)
elif isinstance(module, nn.BatchNorm2d):
nn.init.normal_(module.weight.data, 1.0, 0.02)
nn.init.constant_(module.bias.data, 0)
def forward(self, input):
"""Standard forward."""
return self.main(input)
class ActNorm(nn.Module):
def __init__(self, num_features, logdet=False, affine=True,
allow_reverse_init=False):
assert affine
super().__init__()
self.logdet = logdet
self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
self.allow_reverse_init = allow_reverse_init
self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
def initialize(self, input):
with torch.no_grad():
flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
mean = (
flatten.mean(1)
.unsqueeze(1)
.unsqueeze(2)
.unsqueeze(3)
.permute(1, 0, 2, 3)
)
std = (
flatten.std(1)
.unsqueeze(1)
.unsqueeze(2)
.unsqueeze(3)
.permute(1, 0, 2, 3)
)
self.loc.data.copy_(-mean)
self.scale.data.copy_(1 / (std + 1e-6))
def forward(self, input, reverse=False):
if reverse:
return self.reverse(input)
if len(input.shape) == 2:
input = input[:,:,None,None]
squeeze = True
else:
squeeze = False
_, _, height, width = input.shape
if self.training and self.initialized.item() == 0:
self.initialize(input)
self.initialized.fill_(1)
h = self.scale * (input + self.loc)
if squeeze:
h = h.squeeze(-1).squeeze(-1)
if self.logdet:
log_abs = torch.log(torch.abs(self.scale))
logdet = height*width*torch.sum(log_abs)
logdet = logdet * torch.ones(input.shape[0]).to(input)
return h, logdet
return h
def reverse(self, output):
if self.training and self.initialized.item() == 0:
if not self.allow_reverse_init:
raise RuntimeError(
"Initializing ActNorm in reverse direction is "
"disabled by default. Use allow_reverse_init=True to enable."
)
else:
self.initialize(output)
self.initialized.fill_(1)
if len(output.shape) == 2:
output = output[:,:,None,None]
squeeze = True
else:
squeeze = False
h = output / self.scale - self.loc
if squeeze:
h = h.squeeze(-1).squeeze(-1)
return h |