#### general settings name: train_byol_segformer use_tb_logger: true model: extensibletrainer distortion: sr scale: 1 gpu_ids: [0] fp16: false start_step: -1 checkpointing_enabled: false wandb: false datasets: train: n_workers: 1 batch_size: 96 mode: byol_dataset crop_size: 224 key1: hq key2: hq dataset: mode: imagefolder paths: <> target_size: 224 scale: 1 fetch_alt_image: false skip_lq: true normalize: imagenet networks: generator: type: generator which_model_G: pixel_local_byol image_size: 224 hidden_layer: tail subnet: which_model_G: segformer #### path path: strict_load: true #resume_state: <> steps: generator: training: generator optimizer: lars optimizer_params: # All parameters from appendix J of BYOL. lr: .08 # From BYOL: LR=.2*/256 weight_decay: !!float 1.5e-6 lars_coefficient: .001 momentum: .9 injectors: gen_inj: type: generator generator: generator in: aug1 out: loss losses: byol_loss: type: direct key: loss weight: 1 train: warmup_iter: -1 mega_batch_factor: 2 val_freq: 1000 niter: 300000 # Default LR scheduler options default_lr_scheme: CosineAnnealingLR_Restart T_period: [120000, 120000, 120000] warmup: 10000 eta_min: .01 # Unspecified by the paper.. restarts: [140000, 280000] # Paper says no re-starts, but this scheduler will add them automatically if we don't set them. # likely I won't train this far. restart_weights: [.5, .25] eval: output_state: loss evaluators: single_point_pair_contrastive_eval: for: generator type: single_point_pair_contrastive_eval batch_size: 16 quantity: 96 similar_set_args: path: <> size: 256 dissimilar_set_args: path: <> size: 256 logger: print_freq: 30 save_checkpoint_freq: 1000 visuals: [hq, aug1] visual_debug_rate: 100