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MODE: 1 # 1: train, 2: test, 3: eval, 4: demo_patch
MODEL: 1 # 1: edge model, 2: inpaint model, 3: edge-inpaint model, 4: joint model
MASK: 3 # 1: random block, 2: half, 3: external, 4: (external, random block), 5: (external, random block, half)
EDGE: 1 # 1: canny, 2: external
NMS: 1 # 0: no non-max-suppression, 1: applies non-max-suppression on the external edges by multiplying by Canny
SEED: 10 # random seed
DEVICE: 1 # 0: CPU, 1: GPU
GPU: [0] # list of gpu ids
DEBUG: 0 # turns on debugging mode
VERBOSE: 0 # turns on verbose mode in the output console
SKIP_PHASE2: 0 # When training Inpaint model, 2nd and 3rd phases (model 2--->model 3 ) by order are needed. But we can merge 2nd phase into the 3rd one to speed up (however, lower performance).
TRAIN_FLIST: ./datasets/places2_train.flist
VAL_FLIST: ./datasets/places2_val.flist
TEST_FLIST: ./datasets/places2_test.flist
TRAIN_EDGE_FLIST: ./datasets/places2_edges_train.flist
VAL_EDGE_FLIST: ./datasets/places2_edges_val.flist
TEST_EDGE_FLIST: ./datasets/places2_edges_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: 0.1 # 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
SIGMA: 2 # standard deviation of the Gaussian filter used in Canny edge detector (0: random, -1: no edge)
MAX_ITERS: 2e7 # maximum number of iterations to train the model
EDGE_THRESHOLD: 0.5 # edge detection threshold
L1_LOSS_WEIGHT: 1 # l1 loss weight
FM_LOSS_WEIGHT: 10 # feature-matching loss weight
STYLE_LOSS_WEIGHT: 1 # style loss weight
CONTENT_LOSS_WEIGHT: 1 # perceptual loss weight
INPAINT_ADV_LOSS_WEIGHT: 0.01 # adversarial 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: 12 # 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)
PRINT_INTERVAL: 20 # how many iterations to wait before terminal prints training status (0: never)