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Config file update

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This commit is contained in:
James Betker 2020-05-12 10:07:58 -06:00
parent 62a97c53d1
commit c540244789
5 changed files with 197 additions and 110 deletions
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22
codes/options/test/test_ESRGAN_adrianna_full.yml
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@ -1,26 +1,28 @@
name: RRDB_ESRGAN_x4
name: adrianna
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]
amp_opt_level: O3
datasets:
test_1: # the 1st test dataset
name: set5
name: kayden
mode: LQ
batch_size: 3
dataroot_LQ: ..\..\datasets\adrianna\full_extract
batch_size: 13
dataroot_LQ: ..\..\datasets\kayden\images
start_at: 10000
#### network structures
network_G:
which_model_G: RRDBNet
in_nc: 3
out_nc: 3
nf: 48
nb: 23
which_model_G: ResGen
nf: 256
nb_denoiser: 20
nb_upsampler: 10
upscale_applications: 1
#### path
path:
pretrain_model_G: ../experiments/rrdb_blacked_gan_g.pth
pretrain_model_G: ../experiments/resgen_vgg_disc_vixen_40000.pth

85
codes/options/train/train_ESRGAN_blacked.yml
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@ -1,85 +0,0 @@
#### general settings
name: blacked_fix_and_upconv
use_tb_logger: true
model: srgan
distortion: sr
scale: 4
gpu_ids: [0]
amp_opt_level: O1
#### datasets
datasets:
train:
name: vixcloseup
mode: LQGT
dataroot_GT: K:\4k6k\4k_closeup\hr
dataroot_LQ: K:\4k6k\4k_closeup\lr_corrupted
doCrop: false
use_shuffle: true
n_workers: 0 # per GPU
batch_size: 40
target_size: 256
color: RGB
val:
name: adrianna_val
mode: LQGT
dataroot_GT: E:\4k6k\datasets\adrianna\val\hhq
dataroot_LQ: E:\4k6k\datasets\adrianna\val\hr
#### network structures
network_G:
which_model_G: RRDBNet
in_nc: 3
out_nc: 3
nf: 48
nb: 23
network_D:
which_model_D: discriminator_resnet
in_nc: 3
nf: 48
#### path
path:
pretrain_model_G: ../experiments/rrdb_blacked_gan_g.pth
pretrain_model_D: ~
strict_load: true
resume_state: ../experiments/blacked_fix_and_upconv/training_state/16500.state
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 1e-5
weight_decay_G: 0
beta1_G: 0.9
beta2_G: 0.99
lr_D: !!float 4e-5
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: [5000, 20000, 40000, 60000]
lr_gamma: 0.5
mega_batch_factor: 4
pixel_criterion: l1
pixel_weight: !!float 1e-2
feature_criterion: l1
feature_weight: 0
feature_weight_decay: .9
feature_weight_decay_steps: 501
feature_weight_minimum: 0
gan_type: gan # gan | ragan
gan_weight: 1
D_update_ratio: 1
D_init_iters: 997
manual_seed: 10
val_freq: !!float 5e2
#### logger
logger:
print_freq: 50
save_checkpoint_freq: !!float 5e2

30
codes/options/train/train_ESRGAN_blacked_xl.yml
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@ -12,13 +12,13 @@ datasets:
train:
name: vixcloseup
mode: LQGT
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]
dataroot_GT: E:\4k6k\datasets\vixen\4k_closeup\hr
dataroot_LQ: [E:\4k6k\datasets\vixen\4k_closeup\lr_corrupted, E:\4k6k\datasets\vixen\4k_closeup\lr_c_blurred]
doCrop: false
use_shuffle: true
n_workers: 12 # per GPU
batch_size: 15
target_size: 256
n_workers: 20 # per GPU
batch_size: 32
target_size: 192
color: RGB
val:
name: adrianna_val
@ -29,27 +29,27 @@ datasets:
#### network structures
network_G:
which_model_G: ResGen
nf: 256
nb_denoiser: 20
nb_upsampler: 10
nf: 164
nb_denoiser: 12
nb_upsampler: 8
network_D:
which_model_D: discriminator_resnet_passthrough
nf: 64
nf: 52
#### path
path:
#pretrain_model_G: ../experiments/pretrained_resnet_G.pth
#pretrain_model_D: ~
strict_load: true
resume_state: ../experiments/blacked_fix_and_upconv_xl/training_state/2500.state
resume_state: ../experiments/blacked_fix_and_upconv_xl/training_state/68000.state
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 1e-4
lr_G: !!float 6e-5
weight_decay_G: 0
beta1_G: 0.9
beta2_G: 0.99
lr_D: !!float 2e-4
lr_D: !!float 6e-5
weight_decay_D: 0
beta1_D: 0.9
beta2_D: 0.99
@ -57,9 +57,9 @@ train:
niter: 400000
warmup_iter: -1 # no warm up
lr_steps: [20000, 40000, 50000, 60000]
lr_steps: [20000, 60000, 80000, 100000]
lr_gamma: 0.5
mega_batch_factor: 3
mega_batch_factor: 2
pixel_criterion: l1
pixel_weight: !!float 1e-2
@ -71,7 +71,7 @@ train:
gan_type: gan # gan | ragan
gan_weight: !!float 1e-2
D_update_ratio: 2
D_update_ratio: 1
D_init_iters: 0
manual_seed: 10

83
codes/options/train/train_resgen_movies.yml Normal file
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@ -0,0 +1,83 @@
#### general settings
name: resgen_movies
use_tb_logger: true
model: srgan
distortion: sr
scale: 4
gpu_ids: [0]
amp_opt_level: O1
#### datasets
datasets:
train:
name: movies
mode: LQGT
dataroot_GT: F:\\upsample_reg\\for_training\\hr
dataroot_LQ: F:\\upsample_reg\\for_training\\lr_corrupted
doCrop: false
use_shuffle: true
n_workers: 8 # per GPU
batch_size: 8
target_size: 256
color: RGB
val:
name: movies_val
mode: LQGT
dataroot_GT: F:\\upsample_reg\\for_training\\val\\hr
dataroot_LQ: F:\\upsample_reg\\for_training\\val\\lr
#### network structures
network_G:
which_model_G: ResGenV2
nf: 256
nb_denoiser: 20
nb_upsampler: 10
network_D:
which_model_D: discriminator_resnet_passthrough
nf: 64
#### path
path:
#pretrain_model_G: ~
#pretrain_model_D: ~
strict_load: true
resume_state: ~
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 6e-5
weight_decay_G: 0
beta1_G: 0.9
beta2_G: 0.99
lr_D: !!float 6e-5
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: [20000, 60000, 80000, 100000]
lr_gamma: 0.5
mega_batch_factor: 1
pixel_criterion: l1
pixel_weight: !!float 1e-2
feature_criterion: l1
feature_weight: 1
feature_weight_decay: 1
feature_weight_decay_steps: 1
feature_weight_minimum: 1
gan_type: gan # gan | ragan
gan_weight: !!float 1e-2
D_update_ratio: 1
D_init_iters: 0
manual_seed: 10
val_freq: !!float 5e2
#### logger
logger:
print_freq: 50
save_checkpoint_freq: !!float 5e2

87
codes/options/train/train_vix_corrupt.yml Normal file
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@ -0,0 +1,87 @@
#### general settings
name: train_vix_corrupt
use_tb_logger: true
model: corruptgan
distortion: downsample
scale: 1
gpu_ids: [0]
amp_opt_level: O1
#### datasets
datasets:
train:
name: blacked
mode: downsample
dataroot_GT: K:\\4k6k\\4k_closeup\\lr_corrupted
dataroot_LQ: G:\\data\\pr_upsample\\ultra_lowq\\for_training\\lr
mismatched_Data_OK: true
use_shuffle: true
n_workers: 0 # per GPU
batch_size: 1
target_size: 64
use_flip: false
use_rot: false
doCrop: false
color: RGB
val:
name: blacked_val
mode: downsample
target_size: 64
dataroot_GT: E:\\4k6k\\datasets\\vixen\\val
dataroot_LQ: E:\\4k6k\\datasets\\vixen\\val
#### network structures
network_G:
which_model_G: ResGenV2
nf: 16
nb_denoiser: 5
nb_upsampler: 0
upscale_applications: 0
network_D:
which_model_D: discriminator_vgg_128
in_nc: 3
nf: 16
#### path
path:
pretrain_model_G: ~
pretrain_model_D: ~
resume_state: ~
strict_load: true
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 5e-5
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: [30000, 60000, 100000, 200000]
lr_gamma: 0.5
pixel_criterion: l2
pixel_weight: !!float 1e-2
feature_criterion: l1
feature_weight: 0
gan_type: gan # gan | ragan
gan_weight: !!float 1e-1
mega_batch_factor: 1
D_update_ratio: 1
D_init_iters: -1
manual_seed: 10
val_freq: !!float 5e2
#### logger
logger:
print_freq: 50
save_checkpoint_freq: !!float 5e2