Spaces:

gwang-kim
/

DATID-3D

Runtime error

DATID-3D / eg3d /train.py

f12ab4c 12 months ago

No virus

24.9 kB

	# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
	# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
	#
	# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
	# property and proprietary rights in and to this material, related
	# documentation and any modifications thereto. Any use, reproduction,
	# disclosure or distribution of this material and related documentation
	# without an express license agreement from NVIDIA CORPORATION or
	# its affiliates is strictly prohibited.

	"""Train a GAN using the techniques described in the paper
	"Efficient Geometry-aware 3D Generative Adversarial Networks."

	Code adapted from
	"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, max_size):
	try:
	dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=max_size, 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=str, required=True)
	@click.option('--data', help='Training data', metavar='[ZIP\|DIR]', type=str, required=True)
	@click.option('--data_max_size',help='Training data max size', metavar='INT', type=click.IntRange(min=1), default=None)
	@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=True, 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='noaug', 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), default=2, show_default=True)
	@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=500, show_default=True)
	@click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.IntRange(min=1), default=1, show_default=True)
	@click.option('--snap', help='How often to save snapshots', metavar='TICKS', type=click.IntRange(min=1), default=10, 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('--freeze_dec_sr',help=' ', metavar='BOOL', type=bool, default=False, show_default=True)
	@click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=0), default=3, show_default=True)
	@click.option('-n','--dry-run', help='Print training options and exit', is_flag=True)

	# @click.option('--sr_module', help='Superresolution module', metavar='STR', type=str, required=True)
	@click.option('--neural_rendering_resolution_initial', help='Resolution to render at', metavar='INT', type=click.IntRange(min=1), default=64, required=False)
	@click.option('--neural_rendering_resolution_final', help='Final resolution to render at, if blending', metavar='INT', type=click.IntRange(min=1), required=False, default=None)
	@click.option('--neural_rendering_resolution_fade_kimg', help='Kimg to blend resolution over', metavar='INT', type=click.IntRange(min=0), required=False, default=1000, show_default=True)

	@click.option('--blur_fade_kimg', help='Blur over how many', metavar='INT', type=click.IntRange(min=1), required=False, default=200)
	@click.option('--gen_pose_cond', help='If true, enable generator pose conditioning.', metavar='BOOL', type=bool, required=False, default=False)
	@click.option('--c-scale', help='Scale factor for generator pose conditioning.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=1)
	@click.option('--c-noise', help='Add noise for generator pose conditioning.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=0)
	@click.option('--gpc_reg_prob', help='Strength of swapping regularization. None means no generator pose conditioning, i.e. condition with zeros.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=0.5)
	@click.option('--gpc_reg_fade_kimg', help='Length of swapping prob fade', metavar='INT', type=click.IntRange(min=0), required=False, default=1000)
	@click.option('--disc_c_noise', help='Strength of discriminator pose conditioning regularization, in standard deviations.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=0)
	@click.option('--sr_noise_mode', help='Type of noise for superresolution', metavar='STR', type=click.Choice(['random', 'none']), required=False, default='none')
	@click.option('--resume_blur', help='Enable to blur even on resume', metavar='BOOL', type=bool, required=False, default=False)
	@click.option('--sr_num_fp16_res', help='Number of fp16 layers in superresolution', metavar='INT', type=click.IntRange(min=0), default=4, required=False, show_default=True)
	@click.option('--g_num_fp16_res', help='Number of fp16 layers in generator', metavar='INT', type=click.IntRange(min=0), default=0, required=False, show_default=True)
	@click.option('--d_num_fp16_res', help='Number of fp16 layers in discriminator', metavar='INT', type=click.IntRange(min=0), default=4, required=False, show_default=True)
	@click.option('--sr_first_cutoff', help='First cutoff for AF superresolution', metavar='INT', type=click.IntRange(min=2), default=2, required=False, show_default=True)
	@click.option('--sr_first_stopband', help='First cutoff for AF superresolution', metavar='FLOAT', type=click.FloatRange(min=2), default=2**2.1, required=False, show_default=True)
	@click.option('--style_mixing_prob', help='Style-mixing regularization probability for training.', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0, required=False, show_default=True)
	@click.option('--sr-module', help='Superresolution module override', metavar='STR', type=str, required=False, default=None)
	@click.option('--density_reg', help='Density regularization strength.', metavar='FLOAT', type=click.FloatRange(min=0), default=0.25, required=False, show_default=True)
	@click.option('--density_reg_every', help='lazy density reg', metavar='int', type=click.FloatRange(min=1), default=4, required=False, show_default=True)
	@click.option('--density_reg_p_dist', help='density regularization strength.', metavar='FLOAT', type=click.FloatRange(min=0), default=0.004, required=False, show_default=True)
	@click.option('--reg_type', help='Type of regularization', metavar='STR', type=click.Choice(['l1', 'l1-alt', 'monotonic-detach', 'monotonic-fixed', 'total-variation']), required=False, default='l1')
	@click.option('--decoder_lr_mul', help='decoder learning rate multiplier.', metavar='FLOAT', type=click.FloatRange(min=0), default=1, required=False, show_default=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, max_size=opts.data_max_size)
	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.freeze_dec_sr = opts.freeze_dec_sr
	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 = 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
	c.G_kwargs.class_name = 'training.triplane.TriPlaneGenerator'
	c.D_kwargs.class_name = 'training.dual_discriminator.DualDiscriminator'
	c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions.
	c.loss_kwargs.filter_mode = 'antialiased' # Filter mode for raw images ['antialiased', 'none', float [0-1]]
	c.D_kwargs.disc_c_noise = opts.disc_c_noise # Regularization for discriminator pose conditioning

	if opts.cfg =='cat':
	sr_module = 'training.superresolution.SuperresolutionHybrid8XDC_afhq'
	print("Hello")
	else:
	if c.training_set_kwargs.resolution == 512:
	sr_module = 'training.superresolution.SuperresolutionHybrid8XDC'
	elif c.training_set_kwargs.resolution == 256:
	sr_module = 'training.superresolution.SuperresolutionHybrid4X'
	elif c.training_set_kwargs.resolution == 128:
	sr_module = 'training.superresolution.SuperresolutionHybrid2X'
	else:
	assert False, f"Unsupported resolution {c.training_set_kwargs.resolution}; make a new superresolution module"

	if opts.sr_module != None:
	sr_module = opts.sr_module

	rendering_options = {
	'image_resolution': c.training_set_kwargs.resolution,
	'disparity_space_sampling': False,
	'clamp_mode': 'softplus',
	'superresolution_module': sr_module,
	'c_gen_conditioning_zero': not opts.gen_pose_cond, # if true, fill generator pose conditioning label with dummy zero vector
	'gpc_reg_prob': opts.gpc_reg_prob if opts.gen_pose_cond else None,
	'c_scale': opts.c_scale, # mutliplier for generator pose conditioning label
	'superresolution_noise_mode': opts.sr_noise_mode, # [random or none], whether to inject pixel noise into super-resolution layers
	'density_reg': opts.density_reg, # strength of density regularization
	'density_reg_p_dist': opts.density_reg_p_dist, # distance at which to sample perturbed points for density regularization
	'reg_type': opts.reg_type, # for experimenting with variations on density regularization
	'decoder_lr_mul': opts.decoder_lr_mul, # learning rate multiplier for decoder
	'sr_antialias': True,
	}

	if opts.cfg == 'ffhq':
	rendering_options.update({
	'depth_resolution': 48, # number of uniform samples to take per ray.
	'depth_resolution_importance': 48, # number of importance samples to take per ray.
	'ray_start': 2.25, # near point along each ray to start taking samples.
	'ray_end': 3.3, # far point along each ray to stop taking samples.
	'box_warp': 1, # the side-length of the bounding box spanned by the tri-planes; box_warp=1 means [-0.5, -0.5, -0.5] -> [0.5, 0.5, 0.5].
	'avg_camera_radius': 2.7, # used only in the visualizer to specify camera orbit radius.
	'avg_camera_pivot': [0, 0, 0.2], # used only in the visualizer to control center of camera rotation.
	})
	elif opts.cfg == 'afhq':
	rendering_options.update({
	'depth_resolution': 48,
	'depth_resolution_importance': 48,
	'ray_start': 2.25,
	'ray_end': 3.3,
	'box_warp': 1,
	'avg_camera_radius': 2.7,
	'avg_camera_pivot': [0, 0, -0.06],
	})
	elif opts.cfg == 'cat':
	rendering_options.update({
	'depth_resolution': 48,
	'depth_resolution_importance': 48,
	'ray_start': 2.25,
	'ray_end': 3.3,
	'box_warp': 1,
	'avg_camera_radius': 2.7,
	'avg_camera_pivot': [0, 0, -0.06],
	})
	elif opts.cfg == 'shapenet':
	rendering_options.update({
	'depth_resolution': 64,
	'depth_resolution_importance': 64,
	'ray_start': 0.1,
	'ray_end': 2.6,
	'box_warp': 1.6,
	'white_back': True,
	'avg_camera_radius': 1.7,
	'avg_camera_pivot': [0, 0, 0],
	})
	else:
	assert False, "Need to specify config"



	if opts.density_reg > 0:
	c.G_reg_interval = opts.density_reg_every
	c.G_kwargs.rendering_kwargs = rendering_options
	c.G_kwargs.num_fp16_res = 0
	c.loss_kwargs.blur_init_sigma = 10 # Blur the images seen by the discriminator.
	c.loss_kwargs.blur_fade_kimg = c.batch_size * opts.blur_fade_kimg / 32 # Fade out the blur during the first N kimg.

	c.loss_kwargs.gpc_reg_prob = opts.gpc_reg_prob if opts.gen_pose_cond else None
	c.loss_kwargs.gpc_reg_fade_kimg = opts.gpc_reg_fade_kimg
	c.loss_kwargs.dual_discrimination = True
	c.loss_kwargs.neural_rendering_resolution_initial = opts.neural_rendering_resolution_initial
	c.loss_kwargs.neural_rendering_resolution_final = opts.neural_rendering_resolution_final
	c.loss_kwargs.neural_rendering_resolution_fade_kimg = opts.neural_rendering_resolution_fade_kimg
	c.G_kwargs.sr_num_fp16_res = opts.sr_num_fp16_res

	c.G_kwargs.sr_kwargs = dnnlib.EasyDict(channel_base=opts.cbase, channel_max=opts.cmax, fused_modconv_default='inference_only')

	c.loss_kwargs.style_mixing_prob = opts.style_mixing_prob

	# 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.
	if not opts.resume_blur:
	c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup.
	c.loss_kwargs.gpc_reg_fade_kimg = 0 # Disable swapping 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
	c.G_kwargs.num_fp16_res = opts.g_num_fp16_res
	c.G_kwargs.conv_clamp = 256 if opts.g_num_fp16_res > 0 else None
	c.D_kwargs.num_fp16_res = opts.d_num_fp16_res
	c.D_kwargs.conv_clamp = 256 if opts.d_num_fp16_res > 0 else 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

	#----------------------------------------------------------------------------