# general settings name: test_DAT_x2 model_type: DATModel 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: DAT upscale: 2 in_chans: 3 img_size: 64 img_range: 1. split_size: [8,32] depth: [6,6,6,6,6,6] embed_dim: 180 num_heads: [6,6,6,6,6,6] expansion_factor: 4 resi_connection: '1conv' # path path: pretrain_network_g: experiments/pretrained_models/DAT/DAT_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