forked from mrq/DL-Art-School
179 lines
3.9 KiB
YAML
179 lines
3.9 KiB
YAML
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name: train_div2k_esrgan
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model: extensibletrainer
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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 # <-- Gradient checkpointing. Enable for huge GPU memory savings. Disable for distributed training.
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use_tb_logger: true
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wandb: false
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datasets:
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train:
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n_workers: 2
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batch_size: 16
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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: 128
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force_multiple: 1
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scale: 4
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strict: false
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val:
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name: val
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mode: fullimage
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dataroot_GT: /content/set14
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scale: 4
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networks:
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generator:
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type: generator
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which_model_G: RRDBNet
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in_nc: 3
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out_nc: 3
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initial_stride: 1
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nf: 64
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nb: 23
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scale: 4
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blocks_per_checkpoint: 3
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feature_discriminator:
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type: discriminator
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which_model_D: discriminator_vgg_128_gn
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scale: 2
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nf: 64
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in_nc: 3
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image_size: 96
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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_esrgan/training_state/0.state # <-- Set this to resume from a previous training state.
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steps:
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feature_discriminator:
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training: feature_discriminator
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after: 100000 # Discriminator doesn't "turn-on" until step 100k to allow generator to anneal on PSNR loss.
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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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# "image_patch" injectors support the translational loss below. You can remove them if you remove that loss.
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plq:
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type: image_patch
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patch_size: 24
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in: lq
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out: plq
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phq:
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type: image_patch
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patch_size: 96
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in: hq
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out: phq
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dgen_inj:
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type: generator
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generator: generator
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grad: false
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in: plq
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out: dgen
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losses:
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gan_disc_img:
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type: discriminator_gan
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gan_type: gan
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weight: 1
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#min_loss: .4
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noise: .004
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gradient_penalty: true
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real: phq
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fake: dgen
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generator:
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training: generator
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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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pglq:
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type: image_patch
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patch_size: 24
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in: lq
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out: pglq
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pghq:
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type: image_patch
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patch_size: 96
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in: hq
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out: pghq
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gen_inj:
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type: generator
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generator: generator
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in: pglq
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out: gen
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losses:
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pix:
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type: pix
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weight: .05
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criterion: l1
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real: pghq
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fake: gen
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feature:
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type: feature
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after: 80000 # Perceptual/"feature" loss doesn't turn on until step 80k.
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which_model_F: vgg
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criterion: l1
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weight: 1
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real: pghq
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fake: gen
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gan_gen_img:
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after: 100000
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type: generator_gan
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gan_type: gan
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weight: .02
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noise: .004
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discriminator: feature_discriminator
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fake: gen
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real: pghq
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# Translational loss <- not present in the original ESRGAN paper, but I find it reduces artifacts from the GAN.
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# Feel free to remove. The network will still train well.
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translational:
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type: translational
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after: 80000
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weight: 2
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criterion: l1
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generator: generator
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generator_output_index: 0
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detach_fake: false
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patch_size: 96
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overlap: 64
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real: gen
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fake: ['pglq']
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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
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val_freq: 2000
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# LR scheduler options
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default_lr_scheme: MultiStepLR
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gen_lr_steps: [140000, 180000, 200000, 240000] # LR is halved at these steps. Don't do it until GAN is online.
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lr_gamma: 0.5
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eval:
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output_state: gen
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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: [gen, hq, pglq, pghq]
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visual_debug_rate: 100
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