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inpaint.yml
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inpaint.yml
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# parameters
DATASET: 'celebahq' # 'tmnist', 'dtd', 'places2', 'celeba', 'imagenet', 'cityscapes'
RANDOM_CROP: False
VAL: False
LOG_DIR: full_model_celeba_hq_256
MODEL_RESTORE: '' # '20180115220926508503_jyugpu0_places2_NORMAL_wgan_gp_full_model'
GAN: 'wgan_gp' # 'dcgan', 'lsgan', 'wgan_gp', 'one_wgan_gp'
PRETRAIN_COARSE_NETWORK: False
GAN_LOSS_ALPHA: 0.001 # dcgan: 0.0008, wgan: 0.0005, onegan: 0.001
WGAN_GP_LAMBDA: 10
COARSE_L1_ALPHA: 1.2
L1_LOSS_ALPHA: 1.2
AE_LOSS_ALPHA: 1.2
GAN_WITH_MASK: False
DISCOUNTED_MASK: True
RANDOM_SEED: False
PADDING: 'SAME'
# training
NUM_GPUS: 1
GPU_ID: -1 # -1 indicate select any available one, otherwise select gpu ID, e.g. [0,1,3]
TRAIN_SPE: 10000
MAX_ITERS: 1000000
VIZ_MAX_OUT: 10
GRADS_SUMMARY: False
GRADIENT_CLIP: False
GRADIENT_CLIP_VALUE: 0.1
VAL_PSTEPS: 1000
# 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
GLOBAL_DCGAN_LOSS_ALPHA: 1.
GLOBAL_WGAN_LOSS_ALPHA: 1.
# loss legacy
LOAD_VGG_MODEL: False
VGG_MODEL_FILE: data/model_zoo/vgg16.npz
FEATURE_LOSS: False
GRAMS_LOSS: False
TV_LOSS: False
TV_LOSS_ALPHA: 0.
FEATURE_LOSS_ALPHA: 0.01
GRAMS_LOSS_ALPHA: 50
SPATIAL_DISCOUNTING_GAMMA: 0.9