train.total_kimg = 25000
sched.lod_initial_resolution = 8
sched.G_lrate_dict = {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003}
sched.D_lrate_dict = EasyDict(sched.G_lrate_dict)
# WGAN-GP loss for CelebA-HQ.
#desc += '-wgangp'; G_loss = EasyDict(func_name='training.loss.G_wgan'); D_loss = EasyDict(func_name='training.loss.D_wgan_gp'); sched.G_lrate_dict = {k: min(v, 0.002) for k, v in sched.G_lrate_dict.items()}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict)
# Table 1.
desc += '-tuned-baseline'
G.use_styles = False
G.use_pixel_norm = True
G.use_instance_norm = False
G.mapping_layers = 0
G.truncation_psi = None
G.const_input_layer = False
G.style_mixing_prob = 0.0
G.use_noise = False
desc += '-add-mapping-and-styles'
G.const_input_layer = False
G.style_mixing_prob = 0.0
G.use_noise = False
desc += '-remove-traditional-input'
G.style_mixing_prob = 0.0
G.use_noise = False
desc += '-add-noise-inputs'
G.style_mixing_prob = 0.0
#desc += '-mixing-regularization' # default
# Table 2.
#desc += '-mix0'; G.style_mixing_prob = 0.0
desc += '-1gpu'; submit_config.num_gpus = 1; sched.minibatch_base = 4; sched.minibatch_dict = {4: 128, 8: 128, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}
#desc += '-2gpu'; submit_config.num_gpus = 2; sched.minibatch_base = 8; sched.minibatch_dict = {4: 256, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8}
#desc += '-4gpu'; submit_config.num_gpus = 4; sched.minibatch_base = 16; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16}
#desc += '-8gpu'; submit_config.num_gpus = 8; sched.minibatch_base = 32; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32}
# Default options.
train.total_kimg = 15000
sched.lod_initial_resolution = 8
sched.G_lrate_dict = {128: 0.0015, 256: 0.002, 512: 0.003}
sched.D_lrate_dict = EasyDict(sched.G_lrate_dict)
# WGAN-GP loss for CelebA-HQ.
#desc += '-wgangp'; G_loss = EasyDict(func_name='training.loss.G_wgan'); D_loss = EasyDict(func_name='training.loss.D_wgan_gp'); sched.G_lrate_dict = {k: min(v, 0.002) for k, v in sched.G_lrate_dict.items()}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict)
# Table 1.
desc += '-tuned-baseline'; G.use_styles = False; G.use_pixel_norm = True; G.use_instance_norm = False; G.mapping_layers = 0; G.truncation_psi = None; G.const_input_layer = False; G.style_mixing_prob = 0.0; G.use_noise = False
desc += '-add-mapping-and-styles'; G.const_input_layer = False; G.style_mixing_prob = 0.0; G.use_noise = False
desc += '-remove-traditional-input'; G.style_mixing_prob = 0.0; G.use_noise = False
desc += '-add-noise-inputs'; G.style_mixing_prob = 0.0
desc += '-mixing-regularization' # default
# Table 2.
#desc += '-mix0'; G.style_mixing_prob = 0.0
desc += '-mix50'; G.style_mixing_prob = 0.5
#desc += '-mix90'; G.style_mixing_prob = 0.9 # default
#desc += '-mix100'; G.style_mixing_prob = 1.0
# Table 4.
#desc += '-traditional-0'; G.use_styles = False; G.use_pixel_norm = True; G.use_instance_norm = False; G.mapping_layers = 0; G.truncation_psi = None; G.const_input_layer = False; G.style_mixing_prob = 0.0; G.use_noise = False
desc += '-traditional-8'; G.use_styles = False; G.use_pixel_norm = True; G.use_instance_norm = False; G.mapping_layers = 8; G.truncation_psi = None; G.const_input_layer = False; G.style_mixing_prob = 0.0; G.use_noise = False
#desc += '-stylebased-0'; G.mapping_layers = 0
