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DL-Art-School/codes/options/train/train_ESRGAN_blacked.yml

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Add my own configs
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#### general settings
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name: blacked_fix_and_upconv
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use_tb_logger: true
model: srgan
distortion: sr
scale: 4
gpu_ids: [0]
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.
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amp_opt_level: O1
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#### datasets
datasets:
train:
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name: vixcloseup
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mode: LQGT
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dataroot_GT: K:\4k6k\4k_closeup\hr
dataroot_LQ: K:\4k6k\4k_closeup\lr_corrupted
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doCrop: false
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use_shuffle: true
Add support for passthrough disc/gen Add RRDBNetXL, which performs processing at multiple image sizes. Add DiscResnet_passthrough, which allows passthrough of image at different sizes for discrimination. Adjust the rest of the repo to allow generators that return more than just a single image.
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n_workers: 0 # per GPU
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.
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batch_size: 40
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target_size: 256
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color: RGB
val:
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name: adrianna_val
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mode: LQGT
Fixup upconv for the next attempt!
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dataroot_GT: E:\4k6k\datasets\adrianna\val\hhq
dataroot_LQ: E:\4k6k\datasets\adrianna\val\hr
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#### network structures
network_G:
which_model_G: RRDBNet
in_nc: 3
out_nc: 3
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nf: 48
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nb: 23
network_D:
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which_model_D: discriminator_resnet
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in_nc: 3
Fixup upconv for the next attempt!
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nf: 48
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#### 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.
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pretrain_model_G: ../experiments/rrdb_blacked_gan_g.pth
pretrain_model_D: ~
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strict_load: true
Add support for passthrough disc/gen Add RRDBNetXL, which performs processing at multiple image sizes. Add DiscResnet_passthrough, which allows passthrough of image at different sizes for discrimination. Adjust the rest of the repo to allow generators that return more than just a single image.
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resume_state: ../experiments/blacked_fix_and_upconv/training_state/16500.state
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#### training settings: learning rate scheme, loss
train:
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.
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lr_G: !!float 1e-5
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weight_decay_G: 0
beta1_G: 0.9
beta2_G: 0.99
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.
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lr_D: !!float 4e-5
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weight_decay_D: 0
beta1_D: 0.9
beta2_D: 0.99
lr_scheme: MultiStepLR
niter: 400000
warmup_iter: -1 # no warm up
Enable megabatching
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lr_steps: [5000, 20000, 40000, 60000]
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lr_gamma: 0.5
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.
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mega_batch_factor: 4
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pixel_criterion: l1
pixel_weight: !!float 1e-2
feature_criterion: l1
Enable megabatching
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feature_weight: 0
feature_weight_decay: .9
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.
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feature_weight_decay_steps: 501
feature_weight_minimum: 0
Fixup upconv for the next attempt!
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gan_type: gan # gan | ragan
Enable megabatching
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gan_weight: 1
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D_update_ratio: 1
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.
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D_init_iters: 997
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manual_seed: 10
val_freq: !!float 5e2
#### logger
logger:
print_freq: 50
save_checkpoint_freq: !!float 5e2
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