Enable HighToLowResNet to do a 1:1 transform

codes/models/archs/HighToLowResNet.py

@@ -27,14 +27,18 @@ class HighToLowResNet(nn.Module):
         self.trunk_lores = arch_util.make_layer(basic_block2, nb - 15)
 
         # downsampling
-        if self.downscale == 4:
+        if self.downscale == 4 or self.downscale == 1:
             self.downconv1 = nn.Conv2d(nf, nf, 3, stride=2, padding=1, bias=True)
             self.downconv2 = nn.Conv2d(nf, nf*2, 3, stride=2, padding=1, bias=True)
         else:
             raise EnvironmentError("Requested downscale not supported: %i" % (downscale,))
 
         self.HRconv = nn.Conv2d(nf*2, nf*2, 3, stride=1, padding=1, bias=True)
-        self.conv_last = nn.Conv2d(nf*2, out_nc, 3, stride=1, padding=1, bias=True)
+        if self.downscale == 4:
+            self.conv_last = nn.Conv2d(nf*2, out_nc, 3, stride=1, padding=1, bias=True)
+        else:
+            self.pixel_shuffle = nn.PixelShuffle(4)
+            self.conv_last = nn.Conv2d(int(nf/8), out_nc, 3, stride=1, padding=1, bias=True)
 
         # activation function
         self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
@@ -51,13 +55,22 @@ class HighToLowResNet(nn.Module):
         out = self.lrelu(self.conv_first(out))
         out = self.trunk_hires(out)
 
-        if self.downscale == 4:
+        if self.downscale == 4 or self.downscale == 1:
             out = self.lrelu(self.downconv1(out))
             out = self.trunk_medres(out)
             out = self.lrelu(self.downconv2(out))
             out = self.trunk_lores(out)
 
-        out = self.conv_last(self.lrelu(self.HRconv(out)))
-        base = F.interpolate(x, scale_factor=1/self.downscale, mode='bilinear', align_corners=False)
+        if self.downscale == 1:
+            out = self.lrelu(self.pixel_shuffle(self.HRconv(out)))
+            out = self.conv_last(out)
+        else:
+            out = self.conv_last(self.lrelu(self.HRconv(out)))
+
+        if self.downscale == 1:
+            base = x
+        else:
+            base = F.interpolate(x, scale_factor=1/self.downscale, mode='bilinear', align_corners=False)
+
         out += base
         return out

codes/options/train/train_GAN_blacked_corrupt_via_transform.yml

@@ -1,9 +1,9 @@
 #### general settings
-name: downsample_GAN_blacked
+name: corruptGAN_4k_lqprn_closeup_hq_to_hq
 use_tb_logger: true
-model: srgan
-distortion: sr
-scale: .25
+model: corruptgan
+distortion: downsample
+scale: 1
 gpu_ids: [0]
 amp_opt_level: O1
 
@@ -11,41 +11,41 @@ amp_opt_level: O1
 datasets:
   train:
     name: blacked
-    mode: GTLQ
-    dataroot_GT: ../datasets/blacked/train/hr
-    dataroot_LQ: ../datasets/lqprn/train/lr
-
+    mode: downsample
+    dataroot_GT: K:\\4k6k\\4k_closeup\\hr
+    dataroot_LQ: E:\\4k6k\\adrianna\\for_training\\hr
+    mismatched_Data_OK: true
     use_shuffle: true
-    n_workers: 0  # per GPU
-    batch_size: 7
-    target_size: 64
+    n_workers: 4  # per GPU
+    batch_size: 16
+    target_size: 256
     use_flip: false
     use_rot: false
     color: RGB
   val:
     name: blacked_val
-    mode: LQGT
-    dataroot_GT: ../datasets/vrp/validation/hr
-    dataroot_LQ: ../datasets/vrp/validation/lr
+    mode: downsample
+    target_size: 256
+    dataroot_GT: ../datasets/blacked/val/hr
+    dataroot_LQ: ../datasets/blacked/val/hr
 
 #### network structures
 network_G:
-  which_model_G: RRDBNet
+  which_model_G: HighToLowResNet
   in_nc: 3
   out_nc: 3
   nf: 64
-  nb: 23
+  nb: 56
 network_D:
   which_model_D: discriminator_vgg_128
   in_nc: 3
-  nf: 64
+  nf: 128
 
 #### path
 path:
-  #pretrain_model_G: ../experiments/blacked_gen_20000_epochs.pth
-  #pretrain_model_D: ../experiments/blacked_disc_20000_epochs.pth
-  strict_load: true
+  pretrain_model_G: ~
   resume_state: ~
+  strict_load: true
 
 #### training settings: learning rate scheme, loss
 train:
@@ -61,16 +61,17 @@ train:
 
   niter: 400000
   warmup_iter: -1  # no warm up
-  lr_steps: [10000, 30000, 50000, 70000]
+  lr_steps: [4000, 8000, 12000, 15000, 20000]
   lr_gamma: 0.5
 
   pixel_criterion: l1
   pixel_weight: !!float 1e-2
+  feature_criterion: l1
   feature_weight: 0
   gan_type: ragan  # gan | ragan
-  gan_weight: 1
+  gan_weight: !!float 1e-1
 
-  D_update_ratio: 1
+  D_update_ratio: 2
   D_init_iters: 0
 
   manual_seed: 10