configs/training/ablv2_work_nodil_segmpl.yaml

run_title: ''

training_model:
  kind: default
  visualize_each_iters: 1000
  concat_mask: True
  store_discr_outputs_for_vis: True

losses:
  l1:
    weight_missing: 0
    weight_known: 10
  perceptual:
    weight: 0
  adversarial:
    kind: r1
    weight: 10
    gp_coef: 0.001
    mask_as_fake_target: True
    allow_scale_mask: True
  feature_matching:
    weight: 100
  resnet_pl:
    arch_encoder: resnet50
    weight: 30
    weights_path: ${env:TORCH_HOME}

optimizers:
  generator:
    kind: adam
    lr: 0.001
  discriminator:
    kind: adam
    lr: 0.0001

visualizer:  # override only keys, the rest parameters are kept from defaults/visualizer
  key_order:
    - image
    - predicted_image
    - discr_output_fake
    - discr_output_real
    - inpainted
  rescale_keys:
    - discr_output_fake
    - discr_output_real

defaults:
  - location: docker
  - data: abl-04-256-mh-dist
  - generator: pix2pixhd_global_sigmoid
  - discriminator: pix2pixhd_nlayer
  - visualizer: directory
  - evaluator: default_inpainted
  - trainer: any_gpu_large_ssim_ddp_final
  - hydra: overrides