Add Resnet Discriminator with BN

codes/data/__init__.py

@@ -21,7 +21,7 @@ def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
                                            num_workers=num_workers, sampler=sampler, drop_last=True,
                                            pin_memory=False)
     else:
-        return torch.utils.data.DataLoader(dataset, batch_size=12, shuffle=False, num_workers=3,
+        return torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0,
                                            pin_memory=False)
 
 

codes/models/archs/DiscriminatorResnetBN_arch.py

@@ -0,0 +1,150 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+
+__all__ = ['ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202']
+
+
+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)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+            identity = torch.cat((identity, torch.zeros_like(identity)), 1)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_filters=16, num_classes=10):
+        super(ResNet, self).__init__()
+        self.num_layers = sum(layers)
+        self.inplanes = num_filters
+        self.conv1 = conv3x3(3, num_filters)
+        self.bn1 = nn.BatchNorm2d(num_filters)
+        self.relu = nn.ReLU(inplace=True)
+        self.layer1 = self._make_layer(block, num_filters, layers[0])
+        self.layer2 = self._make_layer(block, num_filters * 2, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, num_filters * 4, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, num_filters * 8, layers[2], stride=2)
+        self.fc1 = nn.Linear(num_filters * 8 * 8 * 8, 64, bias=True)
+        self.fc2 = nn.Linear(64, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        for m in self.modules():
+            if isinstance(m, BasicBlock):
+                nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1:
+            downsample = nn.Sequential(
+                nn.AvgPool2d(1, stride=stride),
+                nn.BatchNorm2d(self.inplanes),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes
+        for _ in range(1, blocks):
+            layers.append(block(planes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = x.view(x.size(0), -1)
+        x = self.relu(self.fc1(x))
+        x = self.fc2(x)
+
+        return x
+
+
+def resnet20(**kwargs):
+    """Constructs a ResNet-20 model.
+    """
+    model = ResNet(BasicBlock, [3, 3, 3], **kwargs)
+    return model
+
+
+def resnet32(**kwargs):
+    """Constructs a ResNet-32 model.
+    """
+    model = ResNet(BasicBlock, [5, 5, 5], **kwargs)
+    return model
+
+
+def resnet44(**kwargs):
+    """Constructs a ResNet-44 model.
+    """
+    model = ResNet(BasicBlock, [7, 7, 7], **kwargs)
+    return model
+
+
+def resnet56(**kwargs):
+    """Constructs a ResNet-56 model.
+    """
+    model = ResNet(BasicBlock, [9, 9, 9], **kwargs)
+    return model
+
+
+def resnet110(**kwargs):
+    """Constructs a ResNet-110 model.
+    """
+    model = ResNet(BasicBlock, [18, 18, 18], **kwargs)
+    return model
+
+
+def resnet1202(**kwargs):
+    """Constructs a ResNet-1202 model.
+    """
+    model = ResNet(BasicBlock, [200, 200, 200], **kwargs)
+    return model

codes/models/archs/DiscriminatorResnet_arch.py

@@ -101,15 +101,15 @@ class FixupResNet(nn.Module):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                bias=False)
         self.bias1 = nn.Parameter(torch.zeros(1))
-        self.relu = nn.ReLU(inplace=True)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
         self.layer1 = self._make_layer(block, 64, layers[0])
         self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
         self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
         self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
         self.bias2 = nn.Parameter(torch.zeros(1))
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
+        self.fc1 = nn.Linear(512 * 2 * 2, 100)
+        self.fc2 = nn.Linear(100, num_classes)
 
         for m in self.modules():
             if isinstance(m, FixupBasicBlock):
@@ -142,7 +142,7 @@ class FixupResNet(nn.Module):
 
     def forward(self, x):
         x = self.conv1(x)
-        x = self.relu(x + self.bias1)
+        x = self.lrelu(x + self.bias1)
         x = self.maxpool(x)
 
         x = self.layer1(x)
@@ -150,9 +150,9 @@ class FixupResNet(nn.Module):
         x = self.layer3(x)
         x = self.layer4(x)
 
-        x = self.avgpool(x)
         x = x.view(x.size(0), -1)
-        x = self.fc(x + self.bias2)
+        x = self.lrelu(self.fc1(x))
+        x = self.fc2(x + self.bias2)
 
         return x
 

codes/models/networks.py

@@ -2,10 +2,12 @@ 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.DiscriminatorResnetBN_arch as DiscriminatorResnetBN_arch
 import models.archs.RRDBNet_arch as RRDBNet_arch
 import models.archs.EDVR_arch as EDVR_arch
 import models.archs.HighToLowResNet as HighToLowResNet
 import models.archs.FlatProcessorNet_arch as FlatProcessorNet_arch
+import models.archs.arch_util as arch_utils
 import math
 
 # Generator
@@ -54,8 +56,7 @@ 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'])
+        netD = DiscriminatorResnetBN_arch.resnet32(num_filters=opt_net['nf'], num_classes=1)
     else:
         raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
     return netD

codes/options/train/train_GAN_blacked_corrupt.yml

@@ -16,8 +16,8 @@ datasets:
     dataroot_LQ: E:\\4k6k\\datasets\\ultra_lowq\\for_training
     mismatched_Data_OK: true
     use_shuffle: true
-    n_workers: 8 # per GPU
-    batch_size: 32
+    n_workers: 0 # per GPU
+    batch_size: 16
     target_size: 64
     use_flip: false
     use_rot: false
@@ -34,31 +34,29 @@ network_G:
   which_model_G: FlatProcessorNet
   in_nc: 3
   out_nc: 3
-  nf: 32
-  ra_blocks: 3
-  assembler_blocks: 2
+  nf: 48
+  ra_blocks: 4
+  assembler_blocks: 3
 
 network_D:
   which_model_D: discriminator_resnet
   in_nc: 3
-  nf: 32
-  trunk_resblocks: 3
-  skip_resblocks: 2
+  nf: 64
 
 #### path
 path:
   pretrain_model_G: ~
-  pretrain_model_D: ~
+  pretrain_model_D: ~ #../experiments/resnet_corrupt_discriminator_fixup.pth
   resume_state: ~
   strict_load: true
 
 #### training settings: learning rate scheme, loss
 train:
-  lr_G: !!float 1e-5
+  lr_G: !!float 1e-4
   weight_decay_G: 0
   beta1_G: 0.9
   beta2_G: 0.99
-  lr_D: !!float 1e-5
+  lr_D: !!float 1e-4
   weight_decay_D: 0
   beta1_D: 0.9
   beta2_D: 0.99
@@ -66,18 +64,18 @@ train:
 
   niter: 400000
   warmup_iter: -1  # no warm up
-  lr_steps: [4000, 8000, 12000, 15000, 20000]
+  lr_steps: [12000, 24000, 36000, 48000, 64000]
   lr_gamma: 0.5
 
-  pixel_criterion: l1
+  pixel_criterion: l2
   pixel_weight: !!float 1e-2
   feature_criterion: l1
   feature_weight: 0
-  gan_type: gan  # gan | ragan
+  gan_type: ragan  # gan | ragan
   gan_weight: !!float 1e-1
 
-  D_update_ratio: 1
-  D_init_iters: 1500
+  D_update_ratio: 2
+  D_init_iters: 1200
 
   manual_seed: 10
   val_freq: !!float 5e2