DL-Art-School/recipes/srflow/train_div2k_rrdb_psnr.yml

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2020-12-04 07:32:48 +00:00
#### general settings
name: train_div2k_rrdb_psnr
use_tb_logger: true
model: extensibletrainer
distortion: sr
scale: 2
gpu_ids: [0]
fp16: false
start_step: 0
checkpointing_enabled: true # <-- Highly recommended for single-GPU training. Will not work with DDP.
wandb: false
datasets:
train:
n_workers: 4
batch_size: 32
name: div2k
mode: single_image_extensible
paths: /content/div2k # <-- Put your path here.
target_size: 128
force_multiple: 1
scale: 4
eval: False
num_corrupts_per_image: 0
strict: false
val:
name: val
mode: fullimage
dataroot_GT: /content/set14
scale: 4
force_multiple: 16
networks:
generator:
type: generator
which_model_G: RRDBNet
in_nc: 3
out_nc: 3
nf: 64
nb: 23
scale: 4
blocks_per_checkpoint: 3
#### path
path:
#pretrain_model_generator: <insert pretrained model path if desired>
strict_load: true
#resume_state: ../experiments/train_div2k_rrdb_psnr/training_state/0.state # <-- Set this to resume from a previous training state.
steps:
generator:
training: generator
optimizer_params:
# Optimizer params
lr: !!float 2e-4
weight_decay: 0
beta1: 0.9
beta2: 0.99
injectors:
gen_inj:
type: generator
generator: generator
in: lq
out: gen
losses:
pix:
type: pix
weight: 1
criterion: l1
real: hq
fake: gen
train:
niter: 500000
warmup_iter: -1
mega_batch_factor: 1 # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
val_freq: 2000
# Default LR scheduler options
default_lr_scheme: MultiStepLR
gen_lr_steps: [50000, 100000, 150000, 200000]
lr_gamma: 0.5
eval:
output_state: gen
logger:
print_freq: 30
save_checkpoint_freq: 1000
visuals: [gen, hq, lq]
visual_debug_rate: 100