config.yml.example

MODE: 1             # 1: train, 2: test, 3: eval
MODEL: 1            # 1: semantic model, 2: manga model, 3: semantic-manga model, 4: joint model
MASK: 3             # 1: regular, 2: iregular, 3: external, 4: (regular, iregular)
SEED: 2021          # random seed
GPU: [0,1,2,3]      # list of gpu ids
DEBUG: 0            # turns on debugging mode
VERBOSE: 0          # turns on verbose mode in the output console
NCOLORS: 1  

TRAIN_FLIST: ./datasets/manga_train.flist
VAL_FLIST: ./datasets/manga_val.flist
TEST_FLIST: ./datasets/manga_test.flist

TRAIN_LINE_FLIST: ./datasets/manga_lines_train.flist
VAL_LINE_FLIST: ./datasets/manga_lines_val.flist
TEST_LINE_FLIST: ./datasets/manga_lines_test.flist

TRAIN_MASK_FLIST: ./datasets/masks_train.flist
VAL_MASK_FLIST: ./datasets/masks_val.flist
TEST_MASK_FLIST: ./datasets/masks_test.flist

LR: 0.0001                    # learning rate
D2G_LR: 0.2                   # discriminator/generator learning rate ratio
BETA1: 0.9                    # adam optimizer beta1
BETA2: 0.999                  # adam optimizer beta2
BATCH_SIZE: 4                 # input batch size for training
INPUT_SIZE: 512               # input image size for training 0 for original size
MAX_ITERS: 2e6                # maximum number of iterations to train the model

SHRIK_ITERS: 5
WEIGHTED_L1_LOSS_WEIGHT: 20   # structural line reconstruction weight
L1_LOSS_WEIGHT: 20            # ScreenVAE map loss weight
CONTENT_LOSS_WEIGHT: 200      # screentone reconstruction weight
BINARY_LOSS_WEIGHT: 1         # binarization loss weight
EDGE_LOSS_WEIGHT: 0           # edge loss weight
INPAINT_ADV_LOSS_WEIGHT: 1    # adversarial loss weight
FM_LOSS_WEIGHT: 5             # feature-matching loss weight

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: 6                # number of images to sample
EVAL_INTERVAL: 0              # how many iterations to wait before model evaluation (0: never)
LOG_INTERVAL: 10              # how many iterations to wait before logging training status (0: never)