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test_RGT_x2.yml
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test_RGT_x2.yml
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# general settings
name: test_RGT_x2
model_type: RGTModel
scale: 2
num_gpu: 1
manual_seed: 10
datasets:
test_1: # the 1st test dataset
task: SR
name: Set5
type: PairedImageDataset
dataroot_gt: datasets/benchmark/Set5/HR
dataroot_lq: datasets/benchmark/Set5/LR_bicubic/X2
filename_tmpl: '{}x2'
io_backend:
type: disk
test_2: # the 2st test dataset
task: SR
name: Set14
type: PairedImageDataset
dataroot_gt: datasets/benchmark/Set14/HR
dataroot_lq: datasets/benchmark/Set14/LR_bicubic/X2
filename_tmpl: '{}x2'
io_backend:
type: disk
test_3: # the 3st test dataset
task: SR
name: B100
type: PairedImageDataset
dataroot_gt: datasets/benchmark/B100/HR
dataroot_lq: datasets/benchmark/B100/LR_bicubic/X2
filename_tmpl: '{}x2'
io_backend:
type: disk
test_4: # the 4st test dataset
task: SR
name: Urban100
type: PairedImageDataset
dataroot_gt: datasets/benchmark/Urban100/HR
dataroot_lq: datasets/benchmark/Urban100/LR_bicubic/X2
filename_tmpl: '{}x2'
io_backend:
type: disk
test_5: # the 5st test dataset
task: SR
name: Manga109
type: PairedImageDataset
dataroot_gt: datasets/benchmark/Manga109/HR
dataroot_lq: datasets/benchmark/Manga109/LR_bicubic/X2
filename_tmpl: '{}_LRBI_x2'
io_backend:
type: disk
# network structures
network_g:
type: RGT
upscale: 2
in_chans: 3
img_size: 64
img_range: 1.
depth: [6,6,6,6,6,6,6,6]
embed_dim: 180
num_heads: [6,6,6,6,6,6,6,6]
mlp_ratio: 2
resi_connection: '1conv'
split_size: [8,32]
c_ratio: 0.5
# path
path:
pretrain_network_g: experiments/pretrained_models/RGT/RGT_x2.pth
strict_load_g: True
# validation settings
val:
save_img: True
suffix: ~ # add suffix to saved images, if None, use exp name
use_chop: False # True to save memory, if img too large
metrics:
psnr: # metric name, can be arbitrary
type: calculate_psnr
crop_border: 2
test_y_channel: True
ssim:
type: calculate_ssim
crop_border: 2
test_y_channel: True