File size: 1,545 Bytes
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swinir:
target: model.swinir.SwinIR
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
dataset:
train:
target: dataset.codeformer.CodeformerDataset
params:
# training file list path
file_list:
file_backend_cfg:
target: dataset.file_backend.HardDiskBackend
out_size: 512
crop_type: center
blur_kernel_size: 41
kernel_list: ['iso', 'aniso']
kernel_prob: [0.5, 0.5]
blur_sigma: [0.1, 12]
downsample_range: [1, 12]
noise_range: [0, 15]
jpeg_range: [30, 100]
val:
target: dataset.codeformer.CodeformerDataset
params:
# validation file list path
file_list:
file_backend_cfg:
target: dataset.file_backend.HardDiskBackend
out_size: 512
crop_type: center
blur_kernel_size: 41
kernel_list: ['iso', 'aniso']
kernel_prob: [0.5, 0.5]
blur_sigma: [0.1, 12]
downsample_range: [1, 12]
noise_range: [0, 15]
jpeg_range: [30, 100]
train:
# experiment directory path
exp_dir:
learning_rate: 1e-4
# total batch size
batch_size: 96
num_workers:
train_steps: 150000
log_every: 50
ckpt_every: 10000
image_every: 1000
val_every: 1000
resume: ~
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