lama / configs /training /lama-regular.yaml
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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:
weight: 30
weights_path: ${env:TORCH_HOME}
optimizers:
generator:
kind: adam
lr: 0.001
discriminator:
kind: adam
lr: 0.0001
visualizer:
key_order:
- image
- predicted_image
- discr_output_fake
- discr_output_real
- inpainted
rescale_keys:
- discr_output_fake
- discr_output_real
kind: directory
outdir: ./visualizer-output/lama-regular/samples
generator:
kind: pix2pixhd_global
input_nc: 4
output_nc: 3
ngf: 64
n_downsampling: 3
n_blocks: 9
conv_kind: default
add_out_act: sigmoid
discriminator:
kind: pix2pixhd_nlayer
input_nc: 3
ndf: 64
n_layers: 4
defaults:
- location: docker
- data: abl-04-256-mh-dist
- evaluator: default_inpainted
- trainer: any_gpu_large_ssim_ddp_final
- hydra: overrides