Discriminator part 1

New discriminator. Includes spectral norming.

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

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
 

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

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