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train.py
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train.py
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""Main entry point for training StyleGAN and ProGAN networks."""
import copy
import dnnlib
from dnnlib import EasyDict
import config
from metrics import metric_base
#----------------------------------------------------------------------------
# Official training configs for StyleGAN, targeted mainly for FFHQ.
if 1:
desc = 'sgan' # Description string included in result subdir name.
train = EasyDict(run_func_name='training.training_loop.training_loop') # Options for training loop.
G = EasyDict(func_name='training.networks_stylegan.G_style') # Options for generator network.
D = EasyDict(func_name='training.networks_stylegan.D_basic') # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for discriminator optimizer.
G_loss = EasyDict(func_name='training.loss.G_logistic_nonsaturating') # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_logistic_simplegp', r1_gamma=10.0) # Options for discriminator loss.
dataset = EasyDict() # Options for load_dataset().
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(size='4k', layout='random') # Options for setup_snapshot_image_grid().
metrics = [metric_base.fid50k] # Options for MetricGroup.
submit_config = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
tf_config = {'rnd.np_random_seed': 1000} # Options for tflib.init_tf().
# Dataset.
desc += '-ffhq'; dataset = EasyDict(tfrecord_dir='ffhq'); train.mirror_augment = True
#desc += '-celebahq'; dataset = EasyDict(tfrecord_dir='celebahq'); train.mirror_augment = True
#desc += '-bedroom'; dataset = EasyDict(tfrecord_dir='lsun-bedroom-full'); train.mirror_augment = False
#desc += '-car'; dataset = EasyDict(tfrecord_dir='lsun-car-512x384'); train.mirror_augment = False
#desc += '-cat'; dataset = EasyDict(tfrecord_dir='lsun-cat-full'); train.mirror_augment = False
# Number of GPUs.
#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 = 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 += '-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
#desc += '-stylebased-1'; G.mapping_layers = 1
#desc += '-stylebased-2'; G.mapping_layers = 2
#desc += '-stylebased-8'; G.mapping_layers = 8 # default
#----------------------------------------------------------------------------
# Official training configs for Progressive GAN, targeted mainly for CelebA-HQ.
if 0:
desc = 'pgan' # Description string included in result subdir name.
train = EasyDict(run_func_name='training.training_loop.training_loop') # Options for training loop.
G = EasyDict(func_name='training.networks_progan.G_paper') # Options for generator network.
D = EasyDict(func_name='training.networks_progan.D_paper') # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for discriminator optimizer.
G_loss = EasyDict(func_name='training.loss.G_wgan') # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_wgan_gp') # Options for discriminator loss.
dataset = EasyDict() # Options for load_dataset().
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(size='1080p', layout='random') # Options for setup_snapshot_image_grid().
metrics = [metric_base.fid50k] # Options for MetricGroup.
submit_config = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
tf_config = {'rnd.np_random_seed': 1000} # Options for tflib.init_tf().
# Dataset (choose one).
desc += '-celebahq'; dataset = EasyDict(tfrecord_dir='celebahq'); train.mirror_augment = True
#desc += '-celeba'; dataset = EasyDict(tfrecord_dir='celeba'); train.mirror_augment = True
#desc += '-cifar10'; dataset = EasyDict(tfrecord_dir='cifar10')
#desc += '-cifar100'; dataset = EasyDict(tfrecord_dir='cifar100')
#desc += '-svhn'; dataset = EasyDict(tfrecord_dir='svhn')
#desc += '-mnist'; dataset = EasyDict(tfrecord_dir='mnist')
#desc += '-mnistrgb'; dataset = EasyDict(tfrecord_dir='mnistrgb')
#desc += '-syn1024rgb'; dataset = EasyDict(class_name='training.dataset.SyntheticDataset', resolution=1024, num_channels=3)
#desc += '-lsun-airplane'; dataset = EasyDict(tfrecord_dir='lsun-airplane-100k'); train.mirror_augment = True
#desc += '-lsun-bedroom'; dataset = EasyDict(tfrecord_dir='lsun-bedroom-100k'); train.mirror_augment = True
#desc += '-lsun-bicycle'; dataset = EasyDict(tfrecord_dir='lsun-bicycle-100k'); train.mirror_augment = True
#desc += '-lsun-bird'; dataset = EasyDict(tfrecord_dir='lsun-bird-100k'); train.mirror_augment = True
#desc += '-lsun-boat'; dataset = EasyDict(tfrecord_dir='lsun-boat-100k'); train.mirror_augment = True
#desc += '-lsun-bottle'; dataset = EasyDict(tfrecord_dir='lsun-bottle-100k'); train.mirror_augment = True
#desc += '-lsun-bridge'; dataset = EasyDict(tfrecord_dir='lsun-bridge-100k'); train.mirror_augment = True
#desc += '-lsun-bus'; dataset = EasyDict(tfrecord_dir='lsun-bus-100k'); train.mirror_augment = True
#desc += '-lsun-car'; dataset = EasyDict(tfrecord_dir='lsun-car-100k'); train.mirror_augment = True
#desc += '-lsun-cat'; dataset = EasyDict(tfrecord_dir='lsun-cat-100k'); train.mirror_augment = True
#desc += '-lsun-chair'; dataset = EasyDict(tfrecord_dir='lsun-chair-100k'); train.mirror_augment = True
#desc += '-lsun-churchoutdoor'; dataset = EasyDict(tfrecord_dir='lsun-churchoutdoor-100k'); train.mirror_augment = True
#desc += '-lsun-classroom'; dataset = EasyDict(tfrecord_dir='lsun-classroom-100k'); train.mirror_augment = True
#desc += '-lsun-conferenceroom'; dataset = EasyDict(tfrecord_dir='lsun-conferenceroom-100k'); train.mirror_augment = True
#desc += '-lsun-cow'; dataset = EasyDict(tfrecord_dir='lsun-cow-100k'); train.mirror_augment = True
#desc += '-lsun-diningroom'; dataset = EasyDict(tfrecord_dir='lsun-diningroom-100k'); train.mirror_augment = True
#desc += '-lsun-diningtable'; dataset = EasyDict(tfrecord_dir='lsun-diningtable-100k'); train.mirror_augment = True
#desc += '-lsun-dog'; dataset = EasyDict(tfrecord_dir='lsun-dog-100k'); train.mirror_augment = True
#desc += '-lsun-horse'; dataset = EasyDict(tfrecord_dir='lsun-horse-100k'); train.mirror_augment = True
#desc += '-lsun-kitchen'; dataset = EasyDict(tfrecord_dir='lsun-kitchen-100k'); train.mirror_augment = True
#desc += '-lsun-livingroom'; dataset = EasyDict(tfrecord_dir='lsun-livingroom-100k'); train.mirror_augment = True
#desc += '-lsun-motorbike'; dataset = EasyDict(tfrecord_dir='lsun-motorbike-100k'); train.mirror_augment = True
#desc += '-lsun-person'; dataset = EasyDict(tfrecord_dir='lsun-person-100k'); train.mirror_augment = True
#desc += '-lsun-pottedplant'; dataset = EasyDict(tfrecord_dir='lsun-pottedplant-100k'); train.mirror_augment = True
#desc += '-lsun-restaurant'; dataset = EasyDict(tfrecord_dir='lsun-restaurant-100k'); train.mirror_augment = True
#desc += '-lsun-sheep'; dataset = EasyDict(tfrecord_dir='lsun-sheep-100k'); train.mirror_augment = True
#desc += '-lsun-sofa'; dataset = EasyDict(tfrecord_dir='lsun-sofa-100k'); train.mirror_augment = True
#desc += '-lsun-tower'; dataset = EasyDict(tfrecord_dir='lsun-tower-100k'); train.mirror_augment = True
#desc += '-lsun-train'; dataset = EasyDict(tfrecord_dir='lsun-train-100k'); train.mirror_augment = True
#desc += '-lsun-tvmonitor'; dataset = EasyDict(tfrecord_dir='lsun-tvmonitor-100k'); train.mirror_augment = True
# Conditioning & snapshot options.
#desc += '-cond'; dataset.max_label_size = 'full' # conditioned on full label
#desc += '-cond1'; dataset.max_label_size = 1 # conditioned on first component of the label
#desc += '-g4k'; grid.size = '4k'
#desc += '-grpc'; grid.layout = 'row_per_class'
# Config presets (choose one).
#desc += '-preset-v1-1gpu'; submit_config.num_gpus = 1; D.mbstd_group_size = 16; sched.minibatch_base = 16; sched.minibatch_dict = {256: 14, 512: 6, 1024: 3}; sched.lod_training_kimg = 800; sched.lod_transition_kimg = 800; train.total_kimg = 19000
desc += '-preset-v2-1gpu'; 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}; sched.G_lrate_dict = {1024: 0.0015}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
#desc += '-preset-v2-2gpus'; 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}; sched.G_lrate_dict = {512: 0.0015, 1024: 0.002}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
#desc += '-preset-v2-4gpus'; submit_config.num_gpus = 4; sched.minibatch_base = 16; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16}; sched.G_lrate_dict = {256: 0.0015, 512: 0.002, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
#desc += '-preset-v2-8gpus'; submit_config.num_gpus = 8; sched.minibatch_base = 32; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32}; 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); train.total_kimg = 12000
# Numerical precision (choose one).
desc += '-fp32'; sched.max_minibatch_per_gpu = {256: 16, 512: 8, 1024: 4}
#desc += '-fp16'; G.dtype = 'float16'; D.dtype = 'float16'; G.pixelnorm_epsilon=1e-4; G_opt.use_loss_scaling = True; D_opt.use_loss_scaling = True; sched.max_minibatch_per_gpu = {512: 16, 1024: 8}
# Disable individual features.
#desc += '-nogrowing'; sched.lod_initial_resolution = 1024; sched.lod_training_kimg = 0; sched.lod_transition_kimg = 0; train.total_kimg = 10000
#desc += '-nopixelnorm'; G.use_pixelnorm = False
#desc += '-nowscale'; G.use_wscale = False; D.use_wscale = False
#desc += '-noleakyrelu'; G.use_leakyrelu = False
#desc += '-nosmoothing'; train.G_smoothing_kimg = 0.0
#desc += '-norepeat'; train.minibatch_repeats = 1
#desc += '-noreset'; train.reset_opt_for_new_lod = False
# Special modes.
#desc += '-BENCHMARK'; sched.lod_initial_resolution = 4; sched.lod_training_kimg = 3; sched.lod_transition_kimg = 3; train.total_kimg = (8*2+1)*3; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
#desc += '-BENCHMARK0'; sched.lod_initial_resolution = 1024; train.total_kimg = 10; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
#desc += '-VERBOSE'; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1; train.network_snapshot_ticks = 100
#desc += '-GRAPH'; train.save_tf_graph = True
#desc += '-HIST'; train.save_weight_histograms = True
#----------------------------------------------------------------------------
# Main entry point for training.
# Calls the function indicated by 'train' using the selected options.
def main():
kwargs = EasyDict(train)
kwargs.update(G_args=G, D_args=D, G_opt_args=G_opt, D_opt_args=D_opt, G_loss_args=G_loss, D_loss_args=D_loss)
kwargs.update(dataset_args=dataset, sched_args=sched, grid_args=grid, metric_arg_list=metrics, tf_config=tf_config)
kwargs.submit_config = copy.deepcopy(submit_config)
kwargs.submit_config.run_dir_root = dnnlib.submission.submit.get_template_from_path(config.result_dir)
kwargs.submit_config.run_dir_ignore += config.run_dir_ignore
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------