/
swinir_ffhq512.yaml
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/
swinir_ffhq512.yaml
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trainer:
target: trainer.TrainerSR
model:
target: models.swinir.SwinIR
ckpt_path: ~
params:
img_size: 64
patch_size: 1
in_chans: 3
embed_dim: 180
depths: [6, 6, 6, 6, 6, 6, 6, 6]
num_heads: [6, 6, 6, 6, 6, 6, 6, 6]
window_size: 8
mlp_ratio: 2
sf: 8
img_range: 1.0
upsampler: "nearest+conv"
resi_connection: "1conv"
unshuffle: True
unshuffle_scale: 8
data:
train:
type: gfpgan
params:
files_txt: /mnt/lustre/share/zsyue/data/FFHQ/files_txt/ffhq512.txt
io_backend:
type: disk
use_hflip: true
mean: [0.0, 0.0, 0.0]
std: [1.0, 1.0, 1.0]
out_size: 512
blur_kernel_size: 41
kernel_list: ['iso', 'aniso']
kernel_prob: [0.5, 0.5]
blur_sigma: [0.1, 15]
downsample_range: [0.8, 32]
noise_range: [0, 20]
jpeg_range: [30, 100]
color_jitter_prob: ~
color_jitter_pt_prob: ~
gray_prob: 0.01
gt_gray: True
need_gt_path: False
val:
type: folder
params:
dir_path: /mnt/lustre/zsyue/projects/DifFace/Restoration/testdata/CelebA-Test/lq
dir_path_extra: /mnt/lustre/zsyue/projects/DifFace/Restoration/testdata/CelebA-Test/hq
transform_type: default
transform_kwargs:
mean: 0.0
std: 1.0
im_exts: png
length: 500
need_path: False
recursive: False
train:
lr: 5e-5
lr_min: 5e-6
batch: [32, 16] # batchsize for training and validation
microbatch: 8
num_workers: 4
prefetch_factor: 2
iterations: 300000
weight_decay: 0
save_freq: 10000
val_freq: ${train.save_freq}
log_freq: [100, 2000, 10]
loss_type: L1
tf_logging: True
local_logging: True