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test_CAT_car_q10.yml
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test_CAT_car_q10.yml
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# general settings
name: test_CAR_CAT_q10
model_type: CATModel
scale: 1
num_gpu: 1
manual_seed: 10
datasets:
test_1: # the 1st test dataset
task: CAR
name: Classic5
type: PairedImageDataset
dataroot_gt: datasets/CAR/classic5/Classic5_HQ
dataroot_lq: datasets/CAR/classic5/Classic5_LQ/10
filename_tmpl: '{}'
io_backend:
type: disk
test_2: # the 2st test dataset
task: CAR
name: LIVE1
type: PairedImageDataset
dataroot_gt: datasets/CAR/LIVE1/LIVE1_HQ
dataroot_lq: datasets/CAR/LIVE1/LIVE1_LQ/10
filename_tmpl: '{}'
io_backend:
type: disk
test_3: # the 3st test dataset
task: CAR
name: Urban100
type: PairedImageDataset
dataroot_gt: datasets/CAR/Urban100/Urban100_HQ
dataroot_lq: datasets/CAR/Urban100/Urban100_LQ/10
filename_tmpl: '{}'
io_backend:
type: disk
# network structures
network_g:
type: CAT
upscale: 1
in_chans: 1
img_size: 128
split_size_0: [2,2,2,4,4,4]
split_size_1: [0,0,0,0,0,0] # The other side is H or W
img_range: 255.
depth: [6,6,6,6,6,6]
embed_dim: 180
num_heads: [6,6,6,6,6,6]
mlp_ratio: 4
resi_connection: '1conv'
block_name: 'CATB_axial'
# path
path:
pretrain_network_g: experiments/pretrained_models/CAR/CAR_CAT_q10.pth
strict_load_g: True
# validation settings
val:
save_img: False
suffix: ~ # add suffix to saved images, if None, use exp name
use_chop: True
metrics:
psnr: # metric name, can be arbitrary
type: calculate_psnr
crop_border: 0
test_y_channel: True
ssim:
type: calculate_ssim
crop_border: 0
test_y_channel: True