DL-Art-School/codes/options/test/test_ESRGAN_adrianna_full.yml

name: RRDB_ESRGAN_x4
suffix: ~  # add suffix to saved images
model: sr
distortion: sr
scale: 4
crop_border: ~  # crop border when evaluation. If None(~), crop the scale pixels
gpu_ids: [0]

datasets:
  test_1:  # the 1st test dataset
    name: set5
    mode: LQ
    batch_size: 16
    dataroot_LQ: ..\..\datasets\adrianna\full_extract

#### network structures
network_G:
  which_model_G: RRDBNet
  in_nc: 3
  out_nc: 3
  nf: 48
  nb: 23

#### path
path:
  pretrain_model_G: ../experiments/rrdb_blacked_gan_g.pth
Add my own configs 2020-04-22 06:37:54 +00:00			`name: RRDB_ESRGAN_x4`
			`suffix: ~ # add suffix to saved images`
			`model: sr`
			`distortion: sr`
			`scale: 4`
			`crop_border: ~ # crop border when evaluation. If None(~), crop the scale pixels`
Fix megabatch scaling, log low and med-res gen images 2020-05-05 14:34:57 +00:00			`gpu_ids: [0]`
Add my own configs 2020-04-22 06:37:54 +00:00
			`datasets:`
			`test_1: # the 1st test dataset`
			`name: set5`
			`mode: LQ`
Fix megabatch scaling, log low and med-res gen images 2020-05-05 14:34:57 +00:00			`batch_size: 16`
			`dataroot_LQ: ..\..\datasets\adrianna\full_extract`
Add my own configs 2020-04-22 06:37:54 +00:00
			`#### network structures`
			`network_G:`
			`which_model_G: RRDBNet`
			`in_nc: 3`
			`out_nc: 3`
Support inference across batches, support inference on cpu, checkpoint This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator. 2020-05-04 14:48:25 +00:00			`nf: 48`
Add my own configs 2020-04-22 06:37:54 +00:00			`nb: 23`

			`#### path`
			`path:`
Support inference across batches, support inference on cpu, checkpoint This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator. 2020-05-04 14:48:25 +00:00			`pretrain_model_G: ../experiments/rrdb_blacked_gan_g.pth`