diff --git a/codes/models/archs/DiscriminatorResnet_arch.py b/codes/models/archs/DiscriminatorResnet_arch.py
new file mode 100644
index 00000000..e30a3ab9
--- /dev/null
+++ b/codes/models/archs/DiscriminatorResnet_arch.py
@@ -0,0 +1,85 @@
+import torch
+import torch.nn as nn
+import torchvision
+import models.archs.arch_util as arch_util
+import functools
+import torch.nn.functional as F
+import torch.nn.utils.spectral_norm as SpectralNorm
+
+# Class that halfs the image size (x4 complexity reduction) and doubles the filter size. Substantial resnet
+# processing is also performed.
+class ResnetDownsampleLayer(nn.Module):
+ def __init__(self, starting_channels: int, number_filters: int, filter_multiplier: int, residual_blocks_input: int, residual_blocks_skip_image: int, total_residual_blocks: int):
+ super(ResnetDownsampleLayer, self).__init__()
+
+ self.skip_image_reducer = SpectralNorm(nn.Conv2d(starting_channels, number_filters, 3, stride=1, padding=1, bias=True))
+ self.skip_image_res_trunk = arch_util.make_layer(functools.partial(arch_util.ResidualBlockSpectralNorm, nf=number_filters, total_residual_blocks=total_residual_blocks), residual_blocks_skip_image)
+
+ self.input_reducer = SpectralNorm(nn.Conv2d(number_filters, number_filters*filter_multiplier, 3, stride=2, padding=1, bias=True))
+ self.res_trunk = arch_util.make_layer(functools.partial(arch_util.ResidualBlockSpectralNorm, nf=number_filters*filter_multiplier, total_residual_blocks=total_residual_blocks), residual_blocks_input)
+
+ self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+ arch_util.initialize_weights([self.input_reducer, self.skip_image_reducer], 1)
+
+ def forward(self, x, skip_image):
+ # Process the skip image first.
+ skip = self.lrelu(self.skip_image_reducer(skip_image))
+ skip = self.skip_image_res_trunk(skip)
+
+ # Concat the processed skip image onto the input and perform processing.
+ out = (x + skip) / 2
+ out = self.lrelu(self.input_reducer(out))
+ out = self.res_trunk(out)
+ return out
+
+class DiscriminatorResnet(nn.Module):
+ # Discriminator that downsamples 5 times with resnet blocks at each layer. On each downsample, the filter size is
+ # increased by a factor of 2. Feeds the output of the convs into a dense for prediction at the logits. Scales the
+ # final dense based on the input image size. Intended for use with input images which are multiples of 32.
+ #
+ # This discriminator also includes provisions to pass an image at various downsample steps in directly. When this
+ # is done with a generator, it will allow much shorter gradient paths between the generator and discriminator. When
+ # no downsampled images are passed into the forward() pass, they will be automatically generated from the source
+ # image using interpolation.
+ #
+ # Uses spectral normalization rather than batch normalization.
+ def __init__(self, in_nc: int, nf: int, input_img_size: int, trunk_resblocks: int, skip_resblocks: int):
+ super(DiscriminatorResnet, self).__init__()
+ self.dimensionalize = nn.Conv2d(in_nc, nf, kernel_size=3, stride=1, padding=1, bias=True)
+
+ # Trunk resblocks are the important things to get right, so use those. 5=number of downsample layers.
+ total_resblocks = trunk_resblocks * 5
+ self.downsample1 = ResnetDownsampleLayer(in_nc, nf, 2, trunk_resblocks, skip_resblocks, total_resblocks)
+ self.downsample2 = ResnetDownsampleLayer(in_nc, nf*2, 2, trunk_resblocks, skip_resblocks, total_resblocks)
+ self.downsample3 = ResnetDownsampleLayer(in_nc, nf*4, 2, trunk_resblocks, skip_resblocks, total_resblocks)
+ # At the bottom layers, we cap the filter multiplier. We want this particular network to focus as much on the
+ # macro-details at higher image dimensionality as it does to the feature details.
+ self.downsample4 = ResnetDownsampleLayer(in_nc, nf*8, 1, trunk_resblocks, skip_resblocks, total_resblocks)
+ self.downsample5 = ResnetDownsampleLayer(in_nc, nf*8, 1, trunk_resblocks, skip_resblocks, total_resblocks)
+ self.downsamplers = [self.downsample1, self.downsample2, self.downsample3, self.downsample4, self.downsample5]
+
+ downsampled_image_size = input_img_size / 32
+ self.linear1 = nn.Linear(int(nf * 8 * downsampled_image_size * downsampled_image_size), 100)
+ self.linear2 = nn.Linear(100, 1)
+
+ # activation function
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+ arch_util.initialize_weights([self.dimensionalize, self.linear1, self.linear2], 1)
+
+ def forward(self, x, skip_images=None):
+ if skip_images is None:
+ # Sythesize them from x.
+ skip_images = []
+ for i in range(len(self.downsamplers)):
+ m = 2 ** i
+ skip_images.append(F.interpolate(x, scale_factor=1 / m, mode='bilinear', align_corners=False))
+
+ fea = self.dimensionalize(x)
+ for skip, d in zip(skip_images, self.downsamplers):
+ fea = d(fea, skip)
+
+ fea = fea.view(fea.size(0), -1)
+ fea = self.lrelu(self.linear1(fea))
+ out = self.linear2(fea)
+ return out
diff --git a/codes/models/archs/arch_util.py b/codes/models/archs/arch_util.py
index e2b4a0b9..c33af559 100644
--- a/codes/models/archs/arch_util.py
+++ b/codes/models/archs/arch_util.py
@@ -2,7 +2,16 @@ import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
+import torch.nn.utils.spectral_norm as SpectralNorm
+from math import sqrt
+def scale_conv_weights_fixup(conv, residual_block_count, m=2):
+ k = conv.kernel_size[0]
+ n = conv.out_channels
+ scaling_factor = residual_block_count ** (-1.0 / (2 * m - 2))
+ sigma = sqrt(2 / (k * k * n)) * scaling_factor
+ conv.weight.data = conv.weight.data * sigma
+ return conv
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
@@ -30,6 +39,89 @@ def make_layer(block, n_layers):
layers.append(block())
return nn.Sequential(*layers)
+def conv3x3(in_planes, out_planes, stride=1):
+ """3x3 convolution with padding"""
+ return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+ padding=1, bias=False)
+
+def conv1x1(in_planes, out_planes, stride=1):
+ """1x1 convolution"""
+ return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+class FixupBasicBlock(nn.Module):
+ expansion = 1
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super(FixupBasicBlock, self).__init__()
+ # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+ self.bias1a = nn.Parameter(torch.zeros(1))
+ self.conv1 = conv3x3(inplanes, planes, stride)
+ self.bias1b = nn.Parameter(torch.zeros(1))
+ self.relu = nn.ReLU(inplace=True)
+ self.bias2a = nn.Parameter(torch.zeros(1))
+ self.conv2 = conv3x3(planes, planes)
+ self.scale = nn.Parameter(torch.ones(1))
+ self.bias2b = nn.Parameter(torch.zeros(1))
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ identity = x
+
+ out = self.conv1(x + self.bias1a)
+ out = self.relu(out + self.bias1b)
+
+ out = self.conv2(out + self.bias2a)
+ out = out * self.scale + self.bias2b
+
+ if self.downsample is not None:
+ identity = self.downsample(x + self.bias1a)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
+class FixupBottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super(FixupBottleneck, self).__init__()
+ # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+ self.bias1a = nn.Parameter(torch.zeros(1))
+ self.conv1 = conv1x1(inplanes, planes)
+ self.bias1b = nn.Parameter(torch.zeros(1))
+ self.bias2a = nn.Parameter(torch.zeros(1))
+ self.conv2 = conv3x3(planes, planes, stride)
+ self.bias2b = nn.Parameter(torch.zeros(1))
+ self.bias3a = nn.Parameter(torch.zeros(1))
+ self.conv3 = conv1x1(planes, planes * self.expansion)
+ self.scale = nn.Parameter(torch.ones(1))
+ self.bias3b = nn.Parameter(torch.zeros(1))
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ identity = x
+
+ out = self.conv1(x + self.bias1a)
+ out = self.relu(out + self.bias1b)
+
+ out = self.conv2(out + self.bias2a)
+ out = self.relu(out + self.bias2b)
+
+ out = self.conv3(out + self.bias3a)
+ out = out * self.scale + self.bias3b
+
+ if self.downsample is not None:
+ identity = self.downsample(x + self.bias1a)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
class ResidualBlock(nn.Module):
'''Residual block with BN
---Conv-BN-ReLU-Conv-+-
@@ -38,6 +130,7 @@ class ResidualBlock(nn.Module):
def __init__(self, nf=64):
super(ResidualBlock, self).__init__()
+ self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.BN1 = nn.BatchNorm2d(nf)
self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
@@ -48,10 +141,33 @@ class ResidualBlock(nn.Module):
def forward(self, x):
identity = x
- out = F.relu(self.BN1(self.conv1(x)), inplace=True)
+ out = self.lrelu(self.BN1(self.conv1(x)))
out = self.BN2(self.conv2(out))
return identity + out
+class ResidualBlockSpectralNorm(nn.Module):
+ '''Residual block with Spectral Normalization.
+ ---SpecConv-ReLU-SpecConv-+-
+ |________________|
+ '''
+
+ def __init__(self, nf, total_residual_blocks):
+ super(ResidualBlockSpectralNorm, self).__init__()
+ self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+ self.conv1 = SpectralNorm(nn.Conv2d(nf, nf, 3, 1, 1, bias=True))
+ self.conv2 = SpectralNorm(nn.Conv2d(nf, nf, 3, 1, 1, bias=True))
+
+ # Initialize first.
+ initialize_weights([self.conv1, self.conv2], 1)
+ # Then perform fixup scaling
+ self.conv1 = scale_conv_weights_fixup(self.conv1, total_residual_blocks)
+ self.conv2 = scale_conv_weights_fixup(self.conv2, total_residual_blocks)
+
+ def forward(self, x):
+ identity = x
+ out = self.lrelu(self.conv1(x))
+ out = self.conv2(out)
+ return identity + out
class ResidualBlock_noBN(nn.Module):
'''Residual block w/o BN
@@ -61,6 +177,7 @@ class ResidualBlock_noBN(nn.Module):
def __init__(self, nf=64):
super(ResidualBlock_noBN, self).__init__()
+ self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
@@ -69,7 +186,7 @@ class ResidualBlock_noBN(nn.Module):
def forward(self, x):
identity = x
- out = F.relu(self.conv1(x), inplace=True)
+ out = self.lrelu(self.conv1(x))
out = self.conv2(out)
return identity + out
diff --git a/codes/models/networks.py b/codes/models/networks.py
index dfb9ad32..75d3cf11 100644
--- a/codes/models/networks.py
+++ b/codes/models/networks.py
@@ -1,6 +1,7 @@
import torch
import models.archs.SRResNet_arch as SRResNet_arch
import models.archs.discriminator_vgg_arch as SRGAN_arch
+import models.archs.DiscriminatorResnet_arch as DiscriminatorResnet_arch
import models.archs.RRDBNet_arch as RRDBNet_arch
import models.archs.EDVR_arch as EDVR_arch
import models.archs.HighToLowResNet as HighToLowResNet
@@ -52,6 +53,9 @@ def define_D(opt):
if which_model == 'discriminator_vgg_128':
netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
+ elif which_model == 'discriminator_resnet':
+ netD = DiscriminatorResnet_arch.DiscriminatorResnet(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_size=img_sz,
+ trunk_resblocks=opt_net['trunk_resblocks'], skip_resblocks=opt_net['skip_resblocks'])
else:
raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
return netD
diff --git a/codes/options/train/train_GAN_blacked_corrupt.yml b/codes/options/train/train_GAN_blacked_corrupt.yml
index d9257128..8f8d0c99 100644
--- a/codes/options/train/train_GAN_blacked_corrupt.yml
+++ b/codes/options/train/train_GAN_blacked_corrupt.yml
@@ -16,7 +16,7 @@ datasets:
dataroot_LQ: E:\\4k6k\\datasets\\ultra_lowq\\for_training
mismatched_Data_OK: true
use_shuffle: true
- n_workers: 4 # per GPU
+ n_workers: 8 # per GPU
batch_size: 32
target_size: 64
use_flip: false
@@ -35,19 +35,21 @@ network_G:
in_nc: 3
out_nc: 3
nf: 32
- ra_blocks: 5
- assembler_blocks: 3
+ ra_blocks: 3
+ assembler_blocks: 2
network_D:
- which_model_D: discriminator_vgg_128
+ which_model_D: discriminator_resnet
in_nc: 3
- nf: 64
+ nf: 32
+ trunk_resblocks: 3
+ skip_resblocks: 2
#### path
path:
- pretrain_model_G: ../experiments/corrupt_flatnet_G.pth
- pretrain_model_D: ../experiments/corrupt_flatnet_D.pth
- resume_state: ../experiments/corruptGAN_4k_lqprn_closeup_flat_net/training_state/3000.state
+ pretrain_model_G: ~
+ pretrain_model_D: ~
+ resume_state: ~
strict_load: true
#### training settings: learning rate scheme, loss
@@ -56,7 +58,7 @@ train:
weight_decay_G: 0
beta1_G: 0.9
beta2_G: 0.99
- lr_D: !!float 4e-5
+ lr_D: !!float 1e-5
weight_decay_D: 0
beta1_D: 0.9
beta2_D: 0.99
@@ -71,11 +73,11 @@ train:
pixel_weight: !!float 1e-2
feature_criterion: l1
feature_weight: 0
- gan_type: ragan # gan | ragan
+ gan_type: gan # gan | ragan
gan_weight: !!float 1e-1
D_update_ratio: 1
- D_init_iters: 0
+ D_init_iters: 1500
manual_seed: 10
val_freq: !!float 5e2