forked from mrq/DL-Art-School
86 lines
1.9 KiB
YAML
86 lines
1.9 KiB
YAML
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#### general settings
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name: train_div2k_byol
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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: 0
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checkpointing_enabled: true # <-- Highly recommended for single-GPU training. Will not work with DDP.
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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: 32
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mode: byol_dataset
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crop_size: 256
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normalize: true
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dataset:
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mode: imagefolder
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paths: /content/div2k # <-- Put your path here. Note: full images.
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target_size: 256
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scale: 1
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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: # <-- Specify your own network to pretrain here.
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which_model_G: spinenet
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arch: 49
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use_input_norm: true
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hidden_layer: endpoint_convs.4.conv # <-- 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_div2k_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_params:
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# Optimizer params
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lr: !!float 3e-4
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weight_decay: 0
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beta1: 0.9
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beta2: 0.99
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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: 1 # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
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val_freq: 2000
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# Default LR scheduler options
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default_lr_scheme: MultiStepLR
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gen_lr_steps: [50000, 100000, 150000, 200000]
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lr_gamma: 0.5
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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, lq, aug1, aug2]
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visual_debug_rate: 100
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