config.yml.example

MODE: 1             # 1: train, 2: test, 3: eval, 4: progressive_inpainting
MASK: 3             # 1: random block, 2: half, 3: external, 4: (external, random block), 5: (external, random block, half)  6: one to one image mask
SEED: 10            # random seed
GPU: [0]            # list of gpu ids
DEBUG: 0           # turns on debugging mode
VERBOSE: 0          # turns on verbose mode in the output console

SAVEIMG: 0           # if save the image in test phase
TRAIN_FLIST: ./datasets/places2_train.flist
VAL_FLIST: ./datasets/places2_test.flist
TEST_FLIST: ./datasets/places2_test.flist    #  celeba_test.flist  ...

TRAIN_MASK_FLIST: ./datasets/mask_train.flist
VAL_MASK_FLIST: ./datasets/mask_train.flist
TEST_MASK_FLIST: ./datasets/mask_train.flist   # masks_test1.flist  masks_test2.flist masks_test3.flist masks_test4.flist

BLOCKS: 2                     # set the res block in each stage
LR: 1e-4                      # learning rate
D2G_LR: 0.1                   # discriminator/generator learning rate ratio
BETA1: 0.0                    # adam optimizer beta1
BETA2: 0.9                    # adam optimizer beta2
BATCH_SIZE: 8                # input batch size for training
INPUT_SIZE: 256               # input image size for training 0 for original size
MAX_ITERS: 2e6                # maximum number of iterations to train the model
MAX_STEPS: 5000               # maximum number of each epoch
MAX_EPOCHES: 100              # maximum number of epoches
LOADWITHEPOCH: 1              # if load epoch when loading model 

L1_LOSS_WEIGHT: 1             # l1 loss weight 1                   1
FM_LOSS_WEIGHT: 0             # feature-matching loss weight 10   0
STYLE_LOSS_WEIGHT: 0          # style loss weight  250           0
CONTENT_LOSS_WEIGHT: 0        # perceptual loss weight  0.1      0
INPAINT_ADV_LOSS_WEIGHT: 1    # adversarial loss weight   0.1      1
# GRADIENT_LOSS_WEIGHT: 1       # gradient loss weight   0
GAN_LOSS: nsgan               # nsgan | lsgan | hinge
GAN_POOL_SIZE: 0              # fake images pool size

SAVE_INTERVAL: 1000           # how many iterations to wait before saving model (0: never)
SAMPLE_INTERVAL: 1000         # how many iterations to wait before sampling (0: never)
SAMPLE_SIZE: 12               # number of images to sample
EVAL_INTERVAL: 20             # How many INTERVAL sample while valuation  (0: never  36000 in places)
LOG_INTERVAL: 10              # how many iterations to wait before logging training status (0: never)
TEST_INTERVAL: 32.85          # how many interval numbers to test   Celeb: 1.9962