#### general settings name: resgen_movies use_tb_logger: true model: srgan distortion: sr scale: 4 gpu_ids: [0] amp_opt_level: O1 #### datasets datasets: train: name: movies mode: LQGT dataroot_GT: F:\\upsample_reg\\for_training\\hr dataroot_LQ: F:\\upsample_reg\\for_training\\lr_corrupted doCrop: false use_shuffle: true n_workers: 8 # per GPU batch_size: 8 target_size: 256 color: RGB val: name: movies_val mode: LQGT dataroot_GT: F:\\upsample_reg\\for_training\\val\\hr dataroot_LQ: F:\\upsample_reg\\for_training\\val\\lr #### network structures network_G: which_model_G: ResGenV2 nf: 256 nb_denoiser: 20 nb_upsampler: 10 network_D: which_model_D: discriminator_resnet_passthrough nf: 64 #### path path: #pretrain_model_G: ~ #pretrain_model_D: ~ strict_load: true resume_state: ~ #### training settings: learning rate scheme, loss train: lr_G: !!float 6e-5 weight_decay_G: 0 beta1_G: 0.9 beta2_G: 0.99 lr_D: !!float 6e-5 weight_decay_D: 0 beta1_D: 0.9 beta2_D: 0.99 lr_scheme: MultiStepLR niter: 400000 warmup_iter: -1 # no warm up lr_steps: [20000, 60000, 80000, 100000] lr_gamma: 0.5 mega_batch_factor: 1 pixel_criterion: l1 pixel_weight: !!float 1e-2 feature_criterion: l1 feature_weight: 1 feature_weight_decay: 1 feature_weight_decay_steps: 1 feature_weight_minimum: 1 gan_type: gan # gan | ragan gan_weight: !!float 1e-2 D_update_ratio: 1 D_init_iters: 0 manual_seed: 10 val_freq: !!float 5e2 #### logger logger: print_freq: 50 save_checkpoint_freq: !!float 5e2