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

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mmsr
2019-08-23 13:42:47 +00:00
#### general settings
name: 003_RRDB_ESRGANx4_DIV2K
use_tb_logger: true
model: srgan
distortion: sr
scale: 4
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gpu_ids: [0]
Fixup upconv for the next attempt!
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amp_opt_level: O1
mmsr
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#### datasets
datasets:
train:
name: DIV2K
mode: LQGT
Fixup upconv for the next attempt!
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dataroot_GT: E:/4k6k/datasets/div2k/DIV2K800_sub
dataroot_LQ: E:/4k6k/datasets/div2k/DIV2K800_sub_bicLRx4
mmsr
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use_shuffle: true
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n_workers: 16 # per GPU
Implement ResGen arch This is a simpler resnet-based generator which performs mutations on an input interspersed with interpolate-upsampling. It is a two part generator: 1) A component that "fixes" LQ images with a long string of resnet blocks. This component is intended to remove compression artifacts and other noise from a LQ image. 2) A component that can double the image size. The idea is that this component be trained so that it can work at most reasonable resolutions, such that it can be repeatedly applied to itself to perform multiple upsamples. The motivation here is to simplify what is being done inside of RRDB. I don't believe the complexity inside of that network is justified.
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batch_size: 16
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target_size: 128
mmsr
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use_flip: true
use_rot: true
color: RGB
val:
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name: div2kval
mmsr
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mode: LQGT
Fixup upconv for the next attempt!
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dataroot_GT: E:/4k6k/datasets/div2k/div2k_valid_hr
dataroot_LQ: E:/4k6k/datasets/div2k/div2k_valid_lr_bicubic
mmsr
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#### network structures
network_G:
Implement ResGen arch This is a simpler resnet-based generator which performs mutations on an input interspersed with interpolate-upsampling. It is a two part generator: 1) A component that "fixes" LQ images with a long string of resnet blocks. This component is intended to remove compression artifacts and other noise from a LQ image. 2) A component that can double the image size. The idea is that this component be trained so that it can work at most reasonable resolutions, such that it can be repeatedly applied to itself to perform multiple upsamples. The motivation here is to simplify what is being done inside of RRDB. I don't believe the complexity inside of that network is justified.
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which_model_G: ResGen
nf: 256
mmsr
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network_D:
Implement ResGen arch This is a simpler resnet-based generator which performs mutations on an input interspersed with interpolate-upsampling. It is a two part generator: 1) A component that "fixes" LQ images with a long string of resnet blocks. This component is intended to remove compression artifacts and other noise from a LQ image. 2) A component that can double the image size. The idea is that this component be trained so that it can work at most reasonable resolutions, such that it can be repeatedly applied to itself to perform multiple upsamples. The motivation here is to simplify what is being done inside of RRDB. I don't believe the complexity inside of that network is justified.
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which_model_D: discriminator_resnet_passthrough
nf: 42
mmsr
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#### path
path:
Fixup upconv for the next attempt!
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pretrain_model_G: ~
mmsr
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strict_load: true
resume_state: ~
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 1e-4
weight_decay_G: 0
beta1_G: 0.9
beta2_G: 0.99
lr_D: !!float 1e-4
weight_decay_D: 0
beta1_D: 0.9
beta2_D: 0.99
lr_scheme: MultiStepLR
niter: 400000
warmup_iter: -1 # no warm up
lr_steps: [50000, 100000, 200000, 300000]
lr_gamma: 0.5
Implement ResGen arch This is a simpler resnet-based generator which performs mutations on an input interspersed with interpolate-upsampling. It is a two part generator: 1) A component that "fixes" LQ images with a long string of resnet blocks. This component is intended to remove compression artifacts and other noise from a LQ image. 2) A component that can double the image size. The idea is that this component be trained so that it can work at most reasonable resolutions, such that it can be repeatedly applied to itself to perform multiple upsamples. The motivation here is to simplify what is being done inside of RRDB. I don't believe the complexity inside of that network is justified.
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mega_batch_factor: 1
mmsr
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pixel_criterion: l1
pixel_weight: !!float 1e-2
feature_criterion: l1
feature_weight: 1
Fixup upconv for the next attempt!
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feature_weight_decay: .98
feature_weight_decay_steps: 500
feature_weight_minimum: .1
gan_type: gan # gan | ragan
mmsr
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gan_weight: !!float 5e-3
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D_update_ratio: 2
mmsr
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D_init_iters: 0
manual_seed: 10
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val_freq: !!float 5e2
mmsr
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#### logger
logger:
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print_freq: 50
save_checkpoint_freq: !!float 5e2
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