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