126 lines
3.0 KiB
YAML
126 lines
3.0 KiB
YAML
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#### general settings
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name: train_div2k_srflow
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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: 4
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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: true
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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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name: div2k
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mode: single_image_extensible
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paths: /content/div2k # <-- Put your path here.
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target_size: 160 # <-- SRFlow trains better with factors of 160 for some reason.
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force_multiple: 1
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scale: 4
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eval: False
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num_corrupts_per_image: 0
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strict: false
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networks:
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generator:
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type: generator
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which_model_G: srflow_orig
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nf: 64
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nb: 23
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K: 16
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scale: 4
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initial_stride: 2
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flow_scale: 4
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train_RRDB: false # <-- Start false. After some time, ~20k-50k steps, set to true. TODO: automate this.
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pretrain_rrdb: ../experiments/pretrained_rrdb.pth # <-- Insert path to your pretrained RRDB here.
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flow:
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patch_size: 160
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K: 16
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L: 3
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noInitialInj: true
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coupling: CondAffineSeparatedAndCond
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additionalFlowNoAffine: 2
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split:
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enable: true
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fea_up0: true
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fea_up-1: true
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stackRRDB:
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blocks: [ 1, 8, 15, 22 ]
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concat: true
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gaussian_loss_weight: 1
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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_srflow/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 2e-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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z_inj:
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type: generator
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generator: generator
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in: [hq, lq, None, None, False]
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out: [z, nll]
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# This is computed solely for visual_dbg - that is, to see what your model is actually doing.
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gen_inj:
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every: 50
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type: generator
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generator: generator
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in: [None, lq, None, .4, True]
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out: [gen]
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losses:
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log_likelihood:
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type: direct
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key: nll
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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: 1000
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# Default LR scheduler options
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default_lr_scheme: MultiStepLR
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gen_lr_steps: [20000, 40000, 80000, 100000, 140000, 180000]
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lr_gamma: 0.5
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eval:
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evaluators:
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# This is the best metric I have come up with for monitoring the training progress of srflow networks. You should
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# feed this evaluator a random set of images from your target distribution.
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gaussian:
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for: generator
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type: flownet_gaussian
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batch_size: 2
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dataset:
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paths: /content/random_100_images
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target_size: 512
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force_multiple: 1
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scale: 4
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eval: False
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num_corrupts_per_image: 0
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corruption_blur_scale: 1
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output_state: eval_gen
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logger:
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print_freq: 30
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save_checkpoint_freq: 500
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visuals: [gen, hq, lq]
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visual_debug_rate: 50
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