289 lines
16 KiB
Python
289 lines
16 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
#
|
|
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
|
# and proprietary rights in and to this software, related documentation
|
|
# and any modifications thereto. Any use, reproduction, disclosure or
|
|
# distribution of this software and related documentation without an express
|
|
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
|
|
|
"""Train a GAN using the techniques described in the paper
|
|
"Alias-Free Generative Adversarial Networks"."""
|
|
|
|
import os
|
|
import click
|
|
import re
|
|
import json
|
|
import tempfile
|
|
import torch
|
|
|
|
import dnnlib
|
|
from training import training_loop
|
|
from metrics import metric_main
|
|
from torch_utils import training_stats
|
|
from torch_utils import custom_ops
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
def subprocess_fn(rank, c, temp_dir):
|
|
dnnlib.util.Logger(file_name=os.path.join(c.run_dir, 'log.txt'), file_mode='a', should_flush=True)
|
|
|
|
# Init torch.distributed.
|
|
if c.num_gpus > 1:
|
|
init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
|
|
if os.name == 'nt':
|
|
init_method = 'file:///' + init_file.replace('\\', '/')
|
|
torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus)
|
|
else:
|
|
init_method = f'file://{init_file}'
|
|
torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus)
|
|
|
|
# Init torch_utils.
|
|
sync_device = torch.device('cuda', rank) if c.num_gpus > 1 else None
|
|
training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
|
|
if rank != 0:
|
|
custom_ops.verbosity = 'none'
|
|
|
|
# Execute training loop.
|
|
training_loop.training_loop(rank=rank, **c)
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
def launch_training(c, desc, outdir, dry_run):
|
|
dnnlib.util.Logger(should_flush=True)
|
|
|
|
# Pick output directory.
|
|
prev_run_dirs = []
|
|
if os.path.isdir(outdir):
|
|
prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]
|
|
prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
|
|
prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
|
|
cur_run_id = max(prev_run_ids, default=-1) + 1
|
|
c.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}')
|
|
assert not os.path.exists(c.run_dir)
|
|
|
|
# Print options.
|
|
print()
|
|
print('Training options:')
|
|
print(json.dumps(c, indent=2))
|
|
print()
|
|
print(f'Output directory: {c.run_dir}')
|
|
print(f'Number of GPUs: {c.num_gpus}')
|
|
print(f'Batch size: {c.batch_size} images')
|
|
print(f'Training duration: {c.total_kimg} kimg')
|
|
print(f'Dataset path: {c.training_set_kwargs.path}')
|
|
print(f'Dataset size: {c.training_set_kwargs.max_size} images')
|
|
print(f'Dataset resolution: {c.training_set_kwargs.resolution}')
|
|
print(f'Dataset labels: {c.training_set_kwargs.use_labels}')
|
|
print(f'Dataset x-flips: {c.training_set_kwargs.xflip}')
|
|
print()
|
|
|
|
# Dry run?
|
|
if dry_run:
|
|
print('Dry run; exiting.')
|
|
return
|
|
|
|
# Create output directory.
|
|
print('Creating output directory...')
|
|
os.makedirs(c.run_dir)
|
|
with open(os.path.join(c.run_dir, 'training_options.json'), 'wt') as f:
|
|
json.dump(c, f, indent=2)
|
|
|
|
# Launch processes.
|
|
print('Launching processes...')
|
|
torch.multiprocessing.set_start_method('spawn')
|
|
with tempfile.TemporaryDirectory() as temp_dir:
|
|
if c.num_gpus == 1:
|
|
subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
|
|
else:
|
|
torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus)
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
def init_dataset_kwargs(data):
|
|
try:
|
|
dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False)
|
|
dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) # Subclass of training.dataset.Dataset.
|
|
dataset_kwargs.resolution = dataset_obj.resolution # Be explicit about resolution.
|
|
dataset_kwargs.use_labels = dataset_obj.has_labels # Be explicit about labels.
|
|
dataset_kwargs.max_size = len(dataset_obj) # Be explicit about dataset size.
|
|
return dataset_kwargs, dataset_obj.name
|
|
except IOError as err:
|
|
raise click.ClickException(f'--data: {err}')
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
def parse_comma_separated_list(s):
|
|
if isinstance(s, list):
|
|
return s
|
|
if s is None or s.lower() == 'none' or s == '':
|
|
return []
|
|
return s.split(',')
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@click.command()
|
|
|
|
# Required.
|
|
@click.option('--outdir', help='Where to save the results', metavar='DIR', required=True)
|
|
@click.option('--cfg', help='Base configuration', type=click.Choice(['stylegan3-t', 'stylegan3-r', 'stylegan2']), required=True)
|
|
@click.option('--data', help='Training data', metavar='[ZIP|DIR]', type=str, required=True)
|
|
@click.option('--gpus', help='Number of GPUs to use', metavar='INT', type=click.IntRange(min=1), required=True)
|
|
@click.option('--batch', help='Total batch size', metavar='INT', type=click.IntRange(min=1), required=True)
|
|
@click.option('--gamma', help='R1 regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), required=True)
|
|
|
|
# Optional features.
|
|
@click.option('--cond', help='Train conditional model', metavar='BOOL', type=bool, default=False, show_default=True)
|
|
@click.option('--mirror', help='Enable dataset x-flips', metavar='BOOL', type=bool, default=False, show_default=True)
|
|
@click.option('--aug', help='Augmentation mode', type=click.Choice(['noaug', 'ada', 'fixed']), default='ada', show_default=True)
|
|
@click.option('--resume', help='Resume from given network pickle', metavar='[PATH|URL]', type=str)
|
|
@click.option('--freezed', help='Freeze first layers of D', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
|
|
|
|
# Misc hyperparameters.
|
|
@click.option('--p', help='Probability for --aug=fixed', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.2, show_default=True)
|
|
@click.option('--target', help='Target value for --aug=ada', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.6, show_default=True)
|
|
@click.option('--batch-gpu', help='Limit batch size per GPU', metavar='INT', type=click.IntRange(min=1))
|
|
@click.option('--cbase', help='Capacity multiplier', metavar='INT', type=click.IntRange(min=1), default=32768, show_default=True)
|
|
@click.option('--cmax', help='Max. feature maps', metavar='INT', type=click.IntRange(min=1), default=512, show_default=True)
|
|
@click.option('--glr', help='G learning rate [default: varies]', metavar='FLOAT', type=click.FloatRange(min=0))
|
|
@click.option('--dlr', help='D learning rate', metavar='FLOAT', type=click.FloatRange(min=0), default=0.002, show_default=True)
|
|
@click.option('--map-depth', help='Mapping network depth [default: varies]', metavar='INT', type=click.IntRange(min=1))
|
|
@click.option('--mbstd-group', help='Minibatch std group size', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True)
|
|
|
|
# Misc settings.
|
|
@click.option('--desc', help='String to include in result dir name', metavar='STR', type=str)
|
|
@click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True)
|
|
@click.option('--kimg', help='Total training duration', metavar='KIMG', type=click.IntRange(min=1), default=25000, show_default=True)
|
|
@click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.IntRange(min=1), default=4, show_default=True)
|
|
@click.option('--snap', help='How often to save snapshots', metavar='TICKS', type=click.IntRange(min=1), default=50, show_default=True)
|
|
@click.option('--seed', help='Random seed', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
|
|
@click.option('--fp32', help='Disable mixed-precision', metavar='BOOL', type=bool, default=False, show_default=True)
|
|
@click.option('--nobench', help='Disable cuDNN benchmarking', metavar='BOOL', type=bool, default=False, show_default=True)
|
|
@click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=3, show_default=True)
|
|
@click.option('-n','--dry-run', help='Print training options and exit', is_flag=True)
|
|
|
|
def main(**kwargs):
|
|
"""Train a GAN using the techniques described in the paper
|
|
"Alias-Free Generative Adversarial Networks".
|
|
|
|
Examples:
|
|
|
|
\b
|
|
# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
|
|
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \\
|
|
--gpus=8 --batch=32 --gamma=8.2 --mirror=1
|
|
|
|
\b
|
|
# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
|
|
python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \\
|
|
--gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \\
|
|
--resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
|
|
|
|
\b
|
|
# Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
|
|
python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \\
|
|
--gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug
|
|
"""
|
|
|
|
# Initialize config.
|
|
opts = dnnlib.EasyDict(kwargs) # Command line arguments.
|
|
c = dnnlib.EasyDict() # Main config dict.
|
|
c.G_kwargs = dnnlib.EasyDict(class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict())
|
|
c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict())
|
|
c.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
|
|
c.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
|
|
c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss')
|
|
c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2)
|
|
|
|
# Training set.
|
|
c.training_set_kwargs, dataset_name = init_dataset_kwargs(data=opts.data)
|
|
if opts.cond and not c.training_set_kwargs.use_labels:
|
|
raise click.ClickException('--cond=True requires labels specified in dataset.json')
|
|
c.training_set_kwargs.use_labels = opts.cond
|
|
c.training_set_kwargs.xflip = opts.mirror
|
|
|
|
# Hyperparameters & settings.
|
|
c.num_gpus = opts.gpus
|
|
c.batch_size = opts.batch
|
|
c.batch_gpu = opts.batch_gpu or opts.batch // opts.gpus
|
|
c.G_kwargs.channel_base = c.D_kwargs.channel_base = opts.cbase
|
|
c.G_kwargs.channel_max = c.D_kwargs.channel_max = opts.cmax
|
|
c.G_kwargs.mapping_kwargs.num_layers = (8 if opts.cfg == 'stylegan2' else 2) if opts.map_depth is None else opts.map_depth
|
|
c.D_kwargs.block_kwargs.freeze_layers = opts.freezed
|
|
c.D_kwargs.epilogue_kwargs.mbstd_group_size = opts.mbstd_group
|
|
c.loss_kwargs.r1_gamma = opts.gamma
|
|
c.G_opt_kwargs.lr = (0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr
|
|
c.D_opt_kwargs.lr = opts.dlr
|
|
c.metrics = opts.metrics
|
|
c.total_kimg = opts.kimg
|
|
c.kimg_per_tick = opts.tick
|
|
c.image_snapshot_ticks = c.network_snapshot_ticks = opts.snap
|
|
c.random_seed = c.training_set_kwargs.random_seed = opts.seed
|
|
c.data_loader_kwargs.num_workers = opts.workers
|
|
|
|
# Sanity checks.
|
|
if c.batch_size % c.num_gpus != 0:
|
|
raise click.ClickException('--batch must be a multiple of --gpus')
|
|
if c.batch_size % (c.num_gpus * c.batch_gpu) != 0:
|
|
raise click.ClickException('--batch must be a multiple of --gpus times --batch-gpu')
|
|
if c.batch_gpu < c.D_kwargs.epilogue_kwargs.mbstd_group_size:
|
|
raise click.ClickException('--batch-gpu cannot be smaller than --mbstd')
|
|
if any(not metric_main.is_valid_metric(metric) for metric in c.metrics):
|
|
raise click.ClickException('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
|
|
|
|
# Base configuration.
|
|
c.ema_kimg = c.batch_size * 10 / 32
|
|
if opts.cfg == 'stylegan2':
|
|
c.G_kwargs.class_name = 'training.networks_stylegan2.Generator'
|
|
c.loss_kwargs.style_mixing_prob = 0.9 # Enable style mixing regularization.
|
|
c.loss_kwargs.pl_weight = 2 # Enable path length regularization.
|
|
c.G_reg_interval = 4 # Enable lazy regularization for G.
|
|
c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions.
|
|
c.loss_kwargs.pl_no_weight_grad = True # Speed up path length regularization by skipping gradient computation wrt. conv2d weights.
|
|
else:
|
|
c.G_kwargs.class_name = 'training.networks_stylegan3.Generator'
|
|
c.G_kwargs.magnitude_ema_beta = 0.5 ** (c.batch_size / (20 * 1e3))
|
|
if opts.cfg == 'stylegan3-r':
|
|
c.G_kwargs.conv_kernel = 1 # Use 1x1 convolutions.
|
|
c.G_kwargs.channel_base *= 2 # Double the number of feature maps.
|
|
c.G_kwargs.channel_max *= 2
|
|
c.G_kwargs.use_radial_filters = True # Use radially symmetric downsampling filters.
|
|
c.loss_kwargs.blur_init_sigma = 10 # Blur the images seen by the discriminator.
|
|
c.loss_kwargs.blur_fade_kimg = c.batch_size * 200 / 32 # Fade out the blur during the first N kimg.
|
|
|
|
# Augmentation.
|
|
if opts.aug != 'noaug':
|
|
c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1)
|
|
if opts.aug == 'ada':
|
|
c.ada_target = opts.target
|
|
if opts.aug == 'fixed':
|
|
c.augment_p = opts.p
|
|
|
|
# Resume.
|
|
if opts.resume is not None:
|
|
c.resume_pkl = opts.resume
|
|
c.ada_kimg = 100 # Make ADA react faster at the beginning.
|
|
c.ema_rampup = None # Disable EMA rampup.
|
|
c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup.
|
|
|
|
# Performance-related toggles.
|
|
if opts.fp32:
|
|
c.G_kwargs.num_fp16_res = c.D_kwargs.num_fp16_res = 0
|
|
c.G_kwargs.conv_clamp = c.D_kwargs.conv_clamp = None
|
|
if opts.nobench:
|
|
c.cudnn_benchmark = False
|
|
|
|
# Description string.
|
|
desc = f'{opts.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
|
|
if opts.desc is not None:
|
|
desc += f'-{opts.desc}'
|
|
|
|
# Launch.
|
|
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
if __name__ == "__main__":
|
|
main() # pylint: disable=no-value-for-parameter
|
|
|
|
#----------------------------------------------------------------------------
|