Random config changes

codes/options/test/test_ESRGAN_adrianna_full.yml

@@ -10,7 +10,7 @@ datasets:
   test_1:  # the 1st test dataset
     name: set5
     mode: LQ
-    batch_size: 16
+    batch_size: 3
     dataroot_LQ: ..\..\datasets\adrianna\full_extract
 
 #### network structures

codes/options/train/train_ESRGAN_blacked_xl.yml

@@ -12,12 +12,12 @@ datasets:
   train:
     name: vixcloseup
     mode: LQGT
-    dataroot_GT: K:\4k6k\4k_closeup\hr
-    dataroot_LQ: K:\4k6k\4k_closeup\lr_corrupted
+    dataroot_GT: E:\4k6k\datasets\4k_closeup\hr
+    dataroot_LQ: [E:\4k6k\datasets\4k_closeup\lr_corrupted, E:\4k6k\datasets\4k_closeup\lr_c_blurred]
     doCrop: false
     use_shuffle: true
-    n_workers: 10  # per GPU
-    batch_size: 16
+    n_workers: 12  # per GPU
+    batch_size: 15
     target_size: 256
     color: RGB
   val:
@@ -30,16 +30,18 @@ datasets:
 network_G:
   which_model_G: ResGen
   nf: 256
+  nb_denoiser: 20
+  nb_upsampler: 10
 network_D:
   which_model_D: discriminator_resnet_passthrough
-  nf: 42
+  nf: 64
 
 #### path
 path:
-  #pretrain_model_G: ../experiments/blacked_fix_and_upconv_xl_part1/models/3000_G.pth
+  #pretrain_model_G: ../experiments/pretrained_resnet_G.pth
   #pretrain_model_D: ~
   strict_load: true
-  resume_state: ~
+  resume_state: ../experiments/blacked_fix_and_upconv_xl/training_state/2500.state
 
 #### training settings: learning rate scheme, loss
 train:
@@ -57,7 +59,7 @@ train:
   warmup_iter: -1  # no warm up
   lr_steps: [20000, 40000, 50000, 60000]
   lr_gamma: 0.5
-  mega_batch_factor: 2
+  mega_batch_factor: 3
 
   pixel_criterion: l1
   pixel_weight: !!float 1e-2
@@ -69,8 +71,8 @@ train:
   gan_type: gan  # gan | ragan
   gan_weight: !!float 1e-2
 
-  D_update_ratio: 1
-  D_init_iters: -1
+  D_update_ratio: 2
+  D_init_iters: 0
 
   manual_seed: 10
   val_freq: !!float 5e2

codes/options/train/train_ESRGAN_res.yml

@@ -16,7 +16,7 @@ datasets:
     dataroot_LQ: E:/4k6k/datasets/div2k/DIV2K800_sub_bicLRx4
 
     use_shuffle: true
-    n_workers: 10  # per GPU
+    n_workers: 0  # per GPU
     batch_size: 24
     target_size: 128
     use_flip: true
@@ -32,6 +32,8 @@ datasets:
 network_G:
   which_model_G: ResGen
   nf: 256
+  nb_denoiser: 2
+  nb_upsampler: 28
 network_D:
   which_model_D: discriminator_resnet_passthrough
   nf: 42
@@ -41,7 +43,7 @@ path:
   #pretrain_model_G: ../experiments/blacked_fix_and_upconv_xl_part1/models/3000_G.pth
   #pretrain_model_D: ~
   strict_load: true
-  resume_state: ~
+  resume_state: ../experiments/esrgan_res/training_state/15500.state
 
 #### training settings: learning rate scheme, loss
 train:
@@ -71,7 +73,7 @@ train:
   gan_type: gan  # gan | ragan
   gan_weight: !!float 1e-2
 
-  D_update_ratio: 1
+  D_update_ratio: 2
   D_init_iters: -1
 
   manual_seed: 10

codes/train.py

@@ -30,7 +30,7 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='options/train/train_ESRGAN_res.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='options/train/train_ESRGAN_blacked_xl.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
@@ -147,7 +147,7 @@ def main():
         current_step = resume_state['iter']
         model.resume_training(resume_state)  # handle optimizers and schedulers
     else:
-        current_step = 0
+        current_step = -1
         start_epoch = 0
 
     #### training