# general settings name: DAPE model_type: DAPEModel num_gpu: auto # set num_gpu: 0 for cpu mode manual_seed: 10 ram_model_path: preset/models/ram_swin_large_14m.pth # dataset and data loader settings datasets: train: name: train_dataset type: DAPEDataset root: ['your training dataset path'] ext: ['*.png'] # data loader num_worker_per_gpu: 8 batch_size_per_gpu: 8 dataset_enlarge_ratio: 1 prefetch_mode: ~ # network structures network_g: ram_swin_bert_lora # path path: pretrain_network_g: ~ strict_load_g: false resume_state: ~ # training settings train: ema_decay: 0 optim_g: type: AdamW lr: !!float 1e-4 weight_decay: 0 betas: [0.9, 0.999] scheduler: type: MultiStepLR milestones: [20000, 30000] gamma: 0.5 total_iter: 300000 warmup_iter: 50 # no warm up # losses cri_feature_opt: type: MSELoss loss_weight: 1.0 reduction: mean # validation settings val: val_freq: !!float 1e8 # !!float 5e3 save_img: false # logging settings logger: print_freq: 1 save_checkpoint_freq: !!float 1e3 # !!float 5e3 use_tb_logger: true wandb: project: ~ resume_id: ~ # dist training settings dist_params: backend: nccl port: 29500