forked from mrq/DL-Art-School
94 lines
2.7 KiB
YAML
94 lines
2.7 KiB
YAML
#### general settings
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name: train_imageset_byol
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use_tb_logger: true
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model: extensibletrainer
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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: 0
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checkpointing_enabled: true # <-- Highly recommended for single-GPU training. May not work in distributed settings.
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wandb: false
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datasets:
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train:
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n_workers: 4
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batch_size: 256 # <-- BYOL trains on very large batch sizes. 256 was the smallest batch size possible before a
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# severe drop off in performance. Other parameters here are set to enable this to train on a
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# single 10GB GPU.
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mode: byol_dataset
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crop_size: 224
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normalize: true
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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: /content/imagenet # <-- Put your path here. Directory should be filled with square images.
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target_size: 224
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scale: 1
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skip_lq: true
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networks:
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generator:
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type: generator
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which_model_G: byol
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image_size: 256
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subnet:
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which_model_G: resnet52 # <-- Specify your own network to pretrain here.
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pretrained: false
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hidden_layer: avgpool # <-- Specify a hidden layer from your network here.
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#### path
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path:
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#pretrain_model_generator: <insert pretrained model path if desired>
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strict_load: true
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#resume_state: ../experiments/train_imageset_byol/training_state/0.state # <-- Set this to resume from a previous training 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: .2 # From BYOL paper: 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, aug2]
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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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niter: 500000
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warmup_iter: -1
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mega_batch_factor: 4 # <-- Gradient accumulation factor. If you are running OOM, increase this to [8].
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# Likewise, if you are running on a 24GB GPU, decrease this to [1] to improve batch stats.
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val_freq: 2000
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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 specifies a different, longer schedule that is not practical for anyone not using
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# 4x V100s+. Modify these parameters if you are.
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restart_weights: [.5, .25]
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eval:
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output_state: loss
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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, aug2]
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visual_debug_rate: 100 |