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