config.yml.example

NAME: example
MODE: 1             # 1: train, 2: test, 3: eval
MASK: 1             # 0: disable 1: enable
GATED: 1            # 0: vanilla conv 1: Gatedconv
NORM: 2             # 0: None 1: BatchNorm 2: InstanceNorm
SEED: 10            # random seed
GPU: [1]            # list of gpu ids
DEBUG: 0            # turns on debugging mode
VERBOSE: 0          # turns on verbose mode in the output console
CLASS_NUM: 12
DISCRIMINATIVE: 1
PRED_DF: 0
PRED_COM: 0
PRED_SEM: 0

TRAIN_BASE_FOLDERS: [./example_data/train]

TEST_BASE_FOLDERS: [./example_data/train]

VALID_BASE_FOLDERS: [./example_data/train]

SUBFOLDERS: [train,gt,mask]

DATASET_PORTION: 1.0
LR_G: 0.0001                  # learning rate
LR_D: 0.0001                  # learning rate

BETA_G: 0.5
BETA_D: 0.5
BETA2: 0.9                    # adam optimizer beta2
BATCH_SIZE: 4                 # input batch size for training
BATCH_FACTOR: 4
MAX_ITERS: 1e8                # maximum number of iterations to train the model
PADDING: replicate                # zeros, circular, constant, replicate
PAD_VALUE: 0                  # only used when padding mode is constant

MASK_LOSS: 0                  # mask out the loss from unknown regions
GAN_LOSS: nsgan               # nsgan | lsgan | hinge
SSC_LOSS: lognll              # lognll | softf1
INPAINT_ADV_LOSS_WEIGHT: 0.1  # adversarial loss weight

SAVE_INTERVAL: 500           # how many iterations to wait before saving model (0: never)
SAMPLE_INTERVAL: 0            # how many iterations to wait before sampling (0: never)
SAMPLE_SIZE: 1               # number of inputs 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)