temp / swinir.yaml
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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
hq_key: jpg
lq_key: hint
# Learning rate.
learning_rate: 1e-4
weight_decay: 0