sun2rain_perl.yaml

# training options
phase: train                  # train or test or guide
dataset: sun2rain             # dataset_name
augment_flag: true            # Image augmentation use or not
GPU: 3                        # ID of GPUs to be used for env variable CUDA_VISIBLE_DEVICES
          
# optimization options
epoch: 10                     # The number of epochs to run
iteration: 100000             # The number of training iterations
batch_size: 1 #4                 # The batch size
print_freq: 1000              # The number of image_print_freq
save_freq: 1000               # The number of ckpt_save_freq
img_freq: 100                 # The number of image summary writing
num_style: 5                  # number of styles to sample
direction: a2b                # direction of style guided image translation
guide_img: guide.jpg          # Style guided image translation

# model options
gan_type: lsgan               # GAN loss type [gan / lsgan]

# optimizer
lr: 0.0001                    # The learning rate

# loss weights
gan_w: 1.0                    # weight of adversarial loss
recon_x_w: 10.0               # weight of image reconstruction loss
recon_s_w: 1.0                # weight of style reconstruction loss
recon_c_w: 1.0                # weight of content reconstruction loss
recon_x_cyc_w: 0.0            # weight of explicit style augmented cycle consistency loss
vgg_w: 1.0                    # weight of domain-invariant perceptual loss
vgg_layer_names: VGG16/conv5_3/Relu:0          # layers in the neural network that we want to use for perceptual loss
vgg_weight_file: vgg16/vgg16_weights_notop.h5 # vgg16_weights_notop.h5 is from Keras or use vgg16.npy; models have to bi in vgg16 folder

# generator
ch: 64                        # base channel number per layer
mlp_dim: 256                  # number of filters in MLP
style_dim: 8                  # length of style code
n_sample: 2                   # number of sampling layers in content encoder
n_res: 4                      # number of residual blocks in content encoder/decoder

# discriminator
n_dis: 4                      # number of discriminator layer
n_scale: 3                    # number of scales

# data options
img_h: 256                    # The size of image hegiht
img_w: 256                    # The size of image width
img_ch: 3                     # The size of image channel
num_workers: 8                # number of data loading threads

# logging options
prefix: 2perl                 # Prefix for all directory names
checkpoint_dir: checkpoint    # Directory name to save the checkpoints
result_dir: results           # Directory name to save the generated images
log_dir: logs                 # Directory name to save training logs
sample_dir: samples           # Directory name to save the samples on training