inpaint.yml

# =========================== Basic Settings ===========================
# machine info
num_gpus_per_job: 1  # number of gpus each job need
num_cpus_per_job: 4  # number of gpus each job need
num_hosts_per_job: 1
memory_per_job: 32  # number of gpus each job need
gpu_type: 'nvidia-tesla-p100'

# parameters
name: places2_gated_conv_v100  # any name
model_restore: ''  # logs/places2_gated_conv
dataset: 'celebahq'  # 'tmnist', 'dtd', 'places2', 'celeba', 'imagenet', 'cityscapes'
random_crop: False  # Set to false when dataset is 'celebahq', meaning only resize the images to img_shapes, instead of crop img_shapes from a larger raw image. This is useful when you train on images with different resolutions like places2. In these cases, please set random_crop to true.
val: False  # true if you want to view validation results in tensorboard
log_dir: logs/full_model_celeba_hq_256

gan: 'sngan'
gan_loss_alpha: 1
gan_with_mask: True
discounted_mask: True
random_seed: False
padding: 'SAME'

# training
train_spe: 4000
max_iters: 100000000
viz_max_out: 10
val_psteps: 2000

# data
data_flist:
  # https://github.com/jiahuiyu/progressive_growing_of_gans_tf
  celebahq: [
    'data/celeba_hq/train_shuffled.flist',
    'data/celeba_hq/validation_static_view.flist'
  ]
  # http://mmlab.ie.cuhk.edu.hk/projects/celeba.html, please to use random_crop: True
  celeba: [
    'data/celeba/train_shuffled.flist',
    'data/celeba/validation_static_view.flist'
  ]
  # http://places2.csail.mit.edu/, please download the high-resolution dataset and use random_crop: True
  places2: [
    'data/places2/train_shuffled.flist',
    'data/places2/validation_static_view.flist'
  ]
  # http://www.image-net.org/, please use random_crop: True
  imagenet: [
    'data/imagenet/train_shuffled.flist',
    'data/imagenet/validation_static_view.flist',
  ]

static_view_size: 30
img_shapes: [256, 256, 3]
height: 128
width: 128
max_delta_height: 32
max_delta_width: 32
batch_size: 16
vertical_margin: 0
horizontal_margin: 0

# loss
ae_loss: True
l1_loss: True
l1_loss_alpha: 1.

# to tune
guided: False
edge_threshold: 0.6