import torch
import torch.nn as nn
from models.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN
import torch.nn.functional as F
from trainer.networks import register_model
from utils.util import checkpoint, opt_get
class Discriminator_VGG_128(nn.Module):
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
def __init__(self, in_nc, nf, input_img_factor=1, extra_conv=False):
super(Discriminator_VGG_128, self).__init__()
# [64, 128, 128]
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
self.bn0_1 = nn.BatchNorm2d(nf, affine=True)
# [64, 64, 64]
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
# [128, 32, 32]
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
# [256, 16, 16]
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
# [512, 8, 8]
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
final_nf = nf * 8
self.extra_conv = extra_conv
if self.extra_conv:
self.conv5_0 = nn.Conv2d(nf * 8, nf * 16, 3, 1, 1, bias=False)
self.bn5_0 = nn.BatchNorm2d(nf * 16, affine=True)
self.conv5_1 = nn.Conv2d(nf * 16, nf * 16, 4, 2, 1, bias=False)
self.bn5_1 = nn.BatchNorm2d(nf * 16, affine=True)
input_img_factor = input_img_factor // 2
final_nf = nf * 16
self.linear1 = nn.Linear(final_nf * 4 * input_img_factor * 4 * input_img_factor, 100)
self.linear2 = nn.Linear(100, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
fea = self.lrelu(self.conv0_0(x))
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
#fea = torch.cat([fea, skip_med], dim=1)
fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
#fea = torch.cat([fea, skip_lo], dim=1)
fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
if self.extra_conv:
fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
fea = fea.contiguous().view(fea.size(0), -1)
fea = self.lrelu(self.linear1(fea))
out = self.linear2(fea)
return out
@register_model
def register_discriminator_vgg_128(opt_net, opt):
return Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=opt_net['image_size'] / 128,
extra_conv=opt_net['extra_conv'])
class Discriminator_VGG_128_GN(nn.Module):
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
def __init__(self, in_nc, nf, input_img_factor=1, do_checkpointing=False, extra_conv=False):
super(Discriminator_VGG_128_GN, self).__init__()
self.do_checkpointing = do_checkpointing
# [64, 128, 128]
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
self.bn0_1 = nn.GroupNorm(8, nf, affine=True)
# [64, 64, 64]
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
# [128, 32, 32]
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
# [256, 16, 16]
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
# [512, 8, 8]
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
self.extra_conv = extra_conv
if extra_conv:
self.conv5_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
self.bn5_0 = nn.GroupNorm(8, nf * 8, affine=True)
self.conv5_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn5_1 = nn.GroupNorm(8, nf * 8, affine=True)
input_img_factor = input_img_factor / 2
final_nf = nf * 8
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 100)
self.linear2 = nn.Linear(100, 1)
def compute_body(self, x):
fea = self.lrelu(self.conv0_0(x))
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
#fea = torch.cat([fea, skip_med], dim=1)
fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
#fea = torch.cat([fea, skip_lo], dim=1)
fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
if self.extra_conv:
fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
return fea
def forward(self, x):
if self.do_checkpointing:
fea = checkpoint(self.compute_body, x)
else:
fea = self.compute_body(x)
fea = fea.contiguous().view(fea.size(0), -1)
fea = self.lrelu(self.linear1(fea))
out = self.linear2(fea)
return out
@register_model
def register_discriminator_vgg_128_gn(opt_net, opt):
return Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'],
input_img_factor=opt_net['image_size'] / 128,
extra_conv=opt_get(opt_net, ['extra_conv'], False),
do_checkpointing=opt_get(opt_net, ['do_checkpointing'], False))
class DiscriminatorVGG448GN(nn.Module):
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
def __init__(self, in_nc, nf, do_checkpointing=False):
super().__init__()
self.do_checkpointing = do_checkpointing
# 448x448
self.convn1_0 = nn.Conv2d(in_nc, nf // 2, 3, 1, 1, bias=True)
self.convn1_1 = nn.Conv2d(nf // 2, nf // 2, 4, 2, 1, bias=False)
self.bnn1_1 = nn.GroupNorm(8, nf // 2, affine=True)
# 224x224 (new head)
self.conv0_0_new = nn.Conv2d(nf // 2, nf, 3, 1, 1, bias=False)
self.bn0_0 = nn.GroupNorm(8, nf, affine=True)
# 224x224 (old head)
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) # Unused.
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
self.bn0_1 = nn.GroupNorm(8, nf, affine=True)
# 112x112
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
# 56x56
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
# 28x28
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
# 14x14
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
# out: 7x7
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
final_nf = nf * 8
self.linear1 = nn.Linear(int(final_nf * 7 * 7), 100)
self.linear2 = nn.Linear(100, 1)
# Assign all new heads to the new param group.2
for m in [self.convn1_0, self.convn1_1, self.bnn1_1, self.conv0_0_new, self.bn0_0]:
for p in m.parameters():
p.PARAM_GROUP = 'new_head'
def compute_body(self, x):
fea = self.lrelu(self.convn1_0(x))
fea = self.lrelu(self.bnn1_1(self.convn1_1(fea)))
fea = self.lrelu(self.bn0_0(self.conv0_0_new(fea)))
# fea = self.lrelu(self.conv0_0(x)) <- replaced
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
return fea
def forward(self, x):
if self.do_checkpointing:
fea = checkpoint(self.compute_body, x)
else:
fea = self.compute_body(x)
fea = fea.contiguous().view(fea.size(0), -1)
fea = self.lrelu(self.linear1(fea))
out = self.linear2(fea)
return out
@register_model
def register_discriminator_vgg_448(opt_net, opt):
return DiscriminatorVGG448GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'])