train.py

# Copyright (c) 2021, NVIDIA CORPORATION.  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
"Training Generative Adversarial Networks with Limited Data"."""
import gettext
import os
import click
import re
import json
import tempfile
import torch
import dnnlib
import typing

from training import training_loop
from metrics import metric_main
from torch_utils import training_stats
from torch_utils import custom_ops
from training.networks import affine_out_in_decomposition_parametrizations
from training.networks import out_in_base_parametrizations, out_in_decomposition_parametrizations


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

class UserError(Exception):
    pass


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


resume_specs = {
    # paper256 (config e), resolution = 512
    'ffhq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl',
    # paper256 (config e), resolution = 512
    'celebahq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl',
    # paper256 (config e), resolution = 512
    'lsundog256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl',
    # stylegan2 (config f), resolution = 512
    'ffhq512': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl',
    # stylegan2 (config f), resolution = 512
    'ffhq1024': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl',
    # Models from original StyleGAN2
    # stylegan2 (config f), resolution = 512
    'afhqcat512': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/afhqcat.pkl',
    # stylegan2 (config f), resolution = 512
    'lsun_car512': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-car-config-f.pkl',
    # stylegan2 (config f), resolution = 256
    'lsun_church_outdoor_train256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-church-config-f.pkl',
    # stylegan2 (config f), resolution = 256
    'lsun_horse256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-horse-config-f.pkl',
}


def setup_training_loop_kwargs(
        # General options (not included in desc).
        gpu=None,      # GPU number to execute, default = None
        gpus=None,     # Number of GPUs: <int>, default = 1 gpu
        snap=None,     # Snapshot interval: <int>, default = 50 ticks
        metrics=None,  # List of metric names: [], ['fid50k_full'] (default), ...
        seed=None,     # Random seed: <int>, default = 0

        # Dataset.
        data=None,     # Training dataset (required): <path>
        cond=None,     # Train conditional model based on dataset labels: <bool>, default = False
        subset=None,   # Train with only N images: <int>, default = all
        mirror=None,   # Augment dataset with x-flips: <bool>, default = False

        # Base config.
        cfg=None,      # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar'
        lrate=None,    # Override lr: <float>
        glrate=None,   # Override Generator lr: <float>
        dlrate=None,   # Override Discriminator lr: <float>
        gamma=None,    # Override R1 gamma: <float>
        kimg=None,     # Override training duration: <int>
        batch=None,    # Override batch size: <int>

        # Discriminator augmentation.
        aug=None,      # Augmentation mode: 'ada' (default), 'noaug', 'fixed'
        p=None,        # Specify p for 'fixed' (required): <float>
        target=None,   # Override ADA target for 'ada': <float>, default = depends on aug
        # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc'
        augpipe=None,

        # Transfer learning.
        resume=None,
        # Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', <file>, <url>
        freezed=None,  # Freeze-D: <int>, default = 0 discriminator layers

        # Performance options (not included in desc).
        fp32=None,     # Disable mixed-precision training: <bool>, default = False
        nhwc=None,     # Use NHWC memory format with FP16: <bool>, default = False
        allow_tf32=None,  # Allow PyTorch to use TF32 for matmul and convolutions: <bool>, default = False
        nobench=None,  # Disable cuDNN benchmarking: <bool>, default = False
        workers=None,  # Override number of DataLoader workers: <int>, default = 3
        generator_requires_grad_parts=None,
        use_domain_modulation=False,
        domain_modulation_parametrization=None,
):
    # noinspection PyBroadException
    try:
        # Check if gpu represented as device id
        gpu = str(int(gpu))
    except:
        gpu = None

    print(f'Using GPU with idx: {gpu}')
    if gpu is not None:
        os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
        os.environ['CUDA_VISIBLE_DEVICES'] = gpu

    print(f'Cuda is available: {torch.cuda.is_available()}. N devices: {torch.cuda.device_count()}')
    
    args = dnnlib.EasyDict()

    # ------------------------------------------
    # General options: gpus, snap, metrics, seed
    # ------------------------------------------

    if gpus is None:
        gpus = 1
    assert isinstance(gpus, int)
    if not (gpus >= 1 and gpus & (gpus - 1) == 0):
        raise UserError('--gpus must be a power of two')
    args.num_gpus = gpus

    if snap is None:
        snap = 50
    assert isinstance(snap, int)
    if snap < 1:
        raise UserError('--snap must be at least 1')
    args.image_snapshot_ticks = snap
    args.network_snapshot_ticks = snap

    if metrics is None:
        metrics = ['fid50k_full']
    assert isinstance(metrics, list)
    if not all(metric_main.is_valid_metric(metric) for metric in metrics):
        raise UserError(
            '\n'.join(
                ['--metrics can only contain the following values:'] +
                metric_main.list_valid_metrics()
            )
        )
    args.metrics = metrics

    if seed is None:
        seed = 0
    assert isinstance(seed, int)
    args.random_seed = seed

    # -----------------------------------
    # Dataset: data, cond, subset, mirror
    # -----------------------------------

    assert data is not None
    assert isinstance(data, str)
    args.training_set_kwargs = dnnlib.EasyDict(
        class_name='training.dataset.ImageFolderDataset', path=data,
        use_labels=True, max_size=None, xflip=False
    )
    args.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, num_workers=3, prefetch_factor=2)
    try:
        training_set = dnnlib.util.construct_class_by_name(
            **args.training_set_kwargs
        )  # subclass of training.dataset.Dataset
        args.training_set_kwargs.resolution = training_set.resolution  # be explicit about resolution
        args.training_set_kwargs.use_labels = training_set.has_labels  # be explicit about labels
        args.training_set_kwargs.max_size = len(training_set)  # be explicit about dataset size
        desc = training_set.name
        del training_set  # conserve memory
    except IOError as err:
        raise UserError(f'--data: {err}')

    if cond is None:
        cond = False
    assert isinstance(cond, bool)
    if cond:
        if not args.training_set_kwargs.use_labels:
            raise UserError('--cond=True requires labels specified in dataset.json')
        desc += '-cond'
    else:
        args.training_set_kwargs.use_labels = False

    if subset is not None:
        assert isinstance(subset, int)
        if not 1 <= subset <= args.training_set_kwargs.max_size:
            raise UserError(f'--subset must be between 1 and {args.training_set_kwargs.max_size}')
        desc += f'-subset{subset}'
        if subset < args.training_set_kwargs.max_size:
            args.training_set_kwargs.max_size = subset
            args.training_set_kwargs.random_seed = args.random_seed
            if args.training_set_kwargs.random_seed:
                desc += f'-rs{args.training_set_kwargs.random_seed}'

    if mirror is None:
        mirror = False
    assert isinstance(mirror, bool)
    if mirror:
        desc += '-mirror'
        args.training_set_kwargs.xflip = True

    # ------------------------------------
    # Base config: cfg, gamma, kimg, batch
    # ------------------------------------

    if cfg is None:
        cfg = 'auto'
    assert isinstance(cfg, str)
    desc += f'-{cfg}'

    cfg_specs = {
        # Populated dynamically based on resolution and GPU count.
        'auto': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=2, fmaps=-1),
        # Uses mixed-precision, unlike the original StyleGAN2.
        'stylegan2': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, lrate=0.002, gamma=10, ema=10, ramp=None, map=8, fmaps=1),
        'paper256': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, lrate=0.0025, gamma=1, ema=20, ramp=None, map=8, fmaps=0.5),
        'paper512': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, lrate=0.0025, gamma=0.5, ema=20, ramp=None, map=8, fmaps=1),
        'paper1024': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, lrate=0.002, gamma=2, ema=10, ramp=None, map=8, fmaps=1),
        'cifar': dict(ref_gpus=2, kimg=100000, mb=64, mbstd=32, lrate=0.0025, gamma=0.01, ema=500, ramp=0.05, map=2, fmaps=1),
    }

    assert cfg in cfg_specs
    spec = dnnlib.EasyDict(cfg_specs[cfg])
    if cfg == 'auto':
        desc += f'{gpus:d}'
        spec.ref_gpus = gpus
        res = args.training_set_kwargs.resolution
        spec.mb = max(min(gpus * min(4096 // res, 32), 64), gpus)  # keep gpu memory consumption at bay
        spec.mbstd = min(spec.mb // gpus, 4)  # other hyperparams behave more predictably if mbstd group size remains fixed
        spec.fmaps = 1 if res >= 512 else 0.5
        spec.lrate = 0.002 if res >= 1024 else 0.0025
        spec.gamma = 0.0002 * (res ** 2) / spec.mb  # heuristic formula
        spec.ema = spec.mb * 10 / 32
    if lrate is not None:
        desc += f'-lrate{lrate}'
        spec.lrate = lrate
    if glrate is not None:
        desc += f'-glrate{glrate}'
        spec.glrate = glrate
    else:
        spec.glrate = spec.lrate
    if dlrate is not None:
        desc += f'-dlrate{dlrate}'
        spec.dlrate = dlrate
    else:
        spec.dlrate = spec.lrate

    args.G_kwargs = dnnlib.EasyDict(
        class_name='training.networks.Generator', z_dim=512, w_dim=512,
        mapping_kwargs=dnnlib.EasyDict(), synthesis_kwargs=dnnlib.EasyDict()
    )
    args.D_kwargs = dnnlib.EasyDict(
        class_name='training.networks.Discriminator', block_kwargs=dnnlib.EasyDict(),
        mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict()
    )
    args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int(spec.fmaps * 32768)
    args.G_kwargs.synthesis_kwargs.channel_max = args.D_kwargs.channel_max = 512
    args.G_kwargs.mapping_kwargs.num_layers = spec.map
    args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 4  # enable mixed-precision training
    args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = 256  # clamp activations to avoid float16 overflow
    args.D_kwargs.epilogue_kwargs.mbstd_group_size = spec.mbstd

    args.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.glrate, betas=[0, 0.99], eps=1e-8)
    args.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.dlrate, betas=[0, 0.99], eps=1e-8)
    args.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss', r1_gamma=spec.gamma)

    args.total_kimg = spec.kimg
    args.batch_size = spec.mb
    args.batch_gpu = spec.mb // spec.ref_gpus
    args.ema_kimg = spec.ema
    args.ema_rampup = spec.ramp

    if cfg == 'cifar':
        args.loss_kwargs.pl_weight = 0  # disable path length regularization
        args.loss_kwargs.style_mixing_prob = 0  # disable style mixing
        args.D_kwargs.architecture = 'orig'  # disable residual skip connections

    if gamma is not None:
        assert isinstance(gamma, float)
        if not gamma >= 0:
            raise UserError('--gamma must be non-negative')
        desc += f'-gamma{gamma:g}'
        args.loss_kwargs.r1_gamma = gamma

    if kimg is not None:
        assert isinstance(kimg, int)
        if not kimg >= 1:
            raise UserError('--kimg must be at least 1')
        desc += f'-kimg{kimg:d}'
        args.total_kimg = kimg

    if batch is not None:
        assert isinstance(batch, int)
        if not (batch >= 1 and batch % gpus == 0):
            raise UserError('--batch must be at least 1 and divisible by --gpus')
        desc += f'-batch{batch}'
        args.batch_size = batch
        args.batch_gpu = batch // gpus

    # ---------------------------------------------------
    # Discriminator augmentation: aug, p, target, augpipe
    # ---------------------------------------------------

    if aug is None:
        aug = 'ada'
    else:
        assert isinstance(aug, str)
        desc += f'-{aug}'

    if aug == 'ada':
        args.ada_target = 0.6

    elif aug == 'noaug':
        pass

    elif aug == 'fixed':
        if p is None:
            raise UserError(f'--aug={aug} requires specifying --p')

    else:
        raise UserError(f'--aug={aug} not supported')

    if p is not None:
        assert isinstance(p, float)
        if aug != 'fixed':
            raise UserError('--p can only be specified with --aug=fixed')
        if not 0 <= p <= 1:
            raise UserError('--p must be between 0 and 1')
        desc += f'-p{p:g}'
        args.augment_p = p

    if target is not None:
        assert isinstance(target, float)
        if aug != 'ada':
            raise UserError('--target can only be specified with --aug=ada')
        if not 0 <= target <= 1:
            raise UserError('--target must be between 0 and 1')
        desc += f'-target{target:g}'
        args.ada_target = target

    assert augpipe is None or isinstance(augpipe, str)
    if augpipe is None:
        augpipe = 'bgc'
    else:
        if aug == 'noaug':
            raise UserError('--augpipe cannot be specified with --aug=noaug')
        desc += f'-{augpipe}'

    augpipe_specs = {
        'blit': dict(xflip=1, rotate90=1, xint=1),
        'geom': dict(scale=1, rotate=1, aniso=1, xfrac=1),
        'color': dict(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
        'filter': dict(imgfilter=1),
        'noise': dict(noise=1),
        'cutout': dict(cutout=1),
        'bg': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1),
        'bgc': dict(
            xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1,
            lumaflip=1, hue=1, saturation=1
        ),
        'bgcf': dict(
            xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1,
            lumaflip=1, hue=1, saturation=1, imgfilter=1
        ),
        'bgcfn': dict(
            xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1,
            lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1
        ),
        'bgcfnc': dict(
            xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1,
            lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1, cutout=1
        ),
    }

    assert augpipe in augpipe_specs
    if aug != 'noaug':
        args.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', **augpipe_specs[augpipe])

    # ----------------------------------
    # Transfer learning: resume, freezed
    # ----------------------------------

    assert resume is None or isinstance(resume, str)
    if resume is None:
        resume = 'noresume'
    elif resume == 'noresume':
        desc += '-noresume'
    elif resume in resume_specs:
        desc += f'-resume{resume}'
        args.resume_pkl = resume_specs[resume]  # predefined url
    else:
        desc += '-resumecustom'
        args.resume_pkl = resume  # custom path or url

    if resume != 'noresume':
        args.ada_kimg = 100  # make ADA react faster at the beginning
        args.ema_rampup = None  # disable EMA rampup

    if freezed is not None:
        assert isinstance(freezed, int)
        if not freezed >= 0:
            raise UserError('--freezed must be non-negative')
        desc += f'-freezed{freezed:d}'
        args.D_kwargs.block_kwargs.freeze_layers = freezed

    # -------------------------------------------------
    # Performance options: fp32, nhwc, nobench, workers
    # -------------------------------------------------

    if fp32 is None:
        fp32 = False
    assert isinstance(fp32, bool)
    if fp32:
        args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 0
        args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = None

    if nhwc is None:
        nhwc = False
    assert isinstance(nhwc, bool)
    if nhwc:
        args.G_kwargs.synthesis_kwargs.fp16_channels_last = args.D_kwargs.block_kwargs.fp16_channels_last = True

    if nobench is None:
        nobench = False
    assert isinstance(nobench, bool)
    if nobench:
        args.cudnn_benchmark = False

    if allow_tf32 is None:
        allow_tf32 = False
    assert isinstance(allow_tf32, bool)
    if allow_tf32:
        args.allow_tf32 = True

    if workers is not None:
        assert isinstance(workers, int)
        if not workers >= 1:
            raise UserError('--workers must be at least 1')
        args.data_loader_kwargs.num_workers = workers

    if generator_requires_grad_parts != ['all']:
        desc += '-Gparts-' + ','.join(generator_requires_grad_parts)
    args.requires_grad_parts = {
        'Gboth': generator_requires_grad_parts,
        'Gmain': generator_requires_grad_parts,
        'Greg': generator_requires_grad_parts,
        'Dboth': ['all'],
        'Dmain': ['all'],
        'Dreg': ['all'],
    }
    args.G_kwargs.synthesis_kwargs['generator_requires_grad_parts'] = generator_requires_grad_parts
    if use_domain_modulation:
        args.G_kwargs.synthesis_kwargs['use_domain_modulation'] = True
        domain_modulation_parametrization = ','.join(domain_modulation_parametrization)
        args.G_kwargs.synthesis_kwargs['domain_modulation_parametrization'] = domain_modulation_parametrization
        desc += '-use-dom-mod'
        if domain_modulation_parametrization != 'multiplicative':
            desc += '-' + domain_modulation_parametrization
    return desc, args


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

def subprocess_fn(rank, args, temp_dir):
    dnnlib.util.Logger(file_name=os.path.join(args.run_dir, 'log.txt'), file_mode='a', should_flush=True)

    # Init torch.distributed.
    if args.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=args.num_gpus
            )
        else:
            init_method = f'file://{init_file}'
            torch.distributed.init_process_group(
                backend='nccl', init_method=init_method, rank=rank,
                world_size=args.num_gpus
            )

    # Init torch_utils.
    sync_device = torch.device('cuda', rank) if args.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, **args)


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

class CommaSeparatedList(click.ParamType):
    name = 'list'

    def __init__(self, choices: typing.Optional[typing.Sequence[str]] = None) -> None:
        """Click cli parameter type that allow to check if all elements in comma sepatared list
            are presented in predefined list of avaliable choises

        Args:
            choices (typing.Optional[typing.Sequence[str]], optional): 
                List of avaliable choises. Defaults to None.
        """
        self.choices = choices

    def convert(self, value, param, ctx):
        _ = param, ctx
        if value is None or value.lower() == 'none' or value == '':
            return []
        values = value.split(',')
        for value in values:
            if self.choices is not None and value not in self.choices:
                choices_str = ", ".join(map(repr, self.choices))
                self.fail(
                    gettext.ngettext(
                        "{value!r} is not {choice}.",
                        "{value!r} is not one of {choices}.",
                        len(self.choices),
                    ).format(value=value, choice=choices_str, choices=choices_str),
                    param,
                    ctx,
                )

        return values


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

@click.command()
@click.pass_context
# General options.
@click.option('--outdir', help='Where to save the results', required=True, metavar='DIR')
@click.option('--gpus', help='Number of GPUs to use [default: 1]', type=int, metavar='INT')
@click.option('--gpu', help='GPU idx to train with [default: None]', type=str)
@click.option('--snap', help='Snapshot interval [default: 50 ticks]', type=int, metavar='INT')
@click.option('--metrics', help='Comma-separated list or "none" [default: fid50k_full]', type=CommaSeparatedList())
@click.option('--seed', help='Random seed [default: 0]', type=int, metavar='INT')
@click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True)
# Dataset.
@click.option('--data', help='Training data (directory or zip)', metavar='PATH', required=True)
@click.option(
    '--cond', help='Train conditional model based on dataset labels [default: false]',
    type=bool, metavar='BOOL'
)
@click.option('--subset', help='Train with only N images [default: all]', type=int, metavar='INT')
@click.option('--mirror', help='Enable dataset x-flips [default: false]', type=bool, metavar='BOOL')
# Base config.
@click.option(
    '--cfg', help='Base config [default: auto]',
    type=click.Choice(['auto', 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar'])
)
@click.option('--lrate', help='Override lr', type=float)
@click.option('--glrate', help='Override Generator lr', type=float)
@click.option('--dlrate', help='Override Discriminator lr', type=float)
@click.option('--gamma', help='Override R1 gamma', type=float)
@click.option('--kimg', help='Override training duration', type=int, metavar='INT')
@click.option('--batch', help='Override batch size', type=int, metavar='INT')
# Discriminator augmentation.
@click.option('--aug', help='Augmentation mode [default: ada]', type=click.Choice(['noaug', 'ada', 'fixed']))
@click.option('--p', help='Augmentation probability for --aug=fixed', type=float)
@click.option('--target', help='ADA target value for --aug=ada', type=float)
@click.option(
    '--augpipe', help='Augmentation pipeline [default: bgc]', type=click.Choice(
        ['blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc', 'bgcf', 'bgcfn', 'bgcfnc']
    )
)
# Transfer learning.
@click.option('--resume', help='Resume training [default: noresume]', metavar='PKL')
@click.option('--freezed', help='Freeze-D [default: 0 layers]', type=int, metavar='INT')
# Performance options.
@click.option('--fp32', help='Disable mixed-precision training', type=bool, metavar='BOOL')
@click.option('--nhwc', help='Use NHWC memory format with FP16', type=bool, metavar='BOOL')
@click.option('--nobench', help='Disable cuDNN benchmarking', type=bool, metavar='BOOL')
@click.option('--allow-tf32', help='Allow PyTorch to use TF32 internally', type=bool, metavar='BOOL')
@click.option('--workers', help='Override number of DataLoader workers', type=int, metavar='INT')
@click.option(
    '--generator-requires-grad-parts',
    help='Which part of generator will be optimized',
    type=CommaSeparatedList(), default='all'
)
@click.option('--use-domain-modulation', help='Whether to use domain modulation offset', is_flag=True)
@click.option(
    '--domain-modulation-parametrization',
    help='Whether to use domain modulation offset', default='multiplicative',
    type=CommaSeparatedList(choices=(
            'multiplicative', 'additive', 'multiplicative_w_space', 'additive_w_space',
            *out_in_base_parametrizations, *out_in_decomposition_parametrizations,
            *affine_out_in_decomposition_parametrizations
    ))
)
def main(ctx, outdir, dry_run, **config_kwargs):
    """Train a GAN using the techniques described in the paper
    "Training Generative Adversarial Networks with Limited Data".

    Examples:

    \b
    # Train with custom dataset using 1 GPU.
    python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1

    \b
    # Train class-conditional CIFAR-10 using 2 GPUs.
    python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \\
        --gpus=2 --cfg=cifar --cond=1

    \b
    # Transfer learn MetFaces from FFHQ using 4 GPUs.
    python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \\
        --gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10

    \b
    # Reproduce original StyleGAN2 config F.
    python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \\
        --gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug

    \b
    Base configs (--cfg):
      auto       Automatically select reasonable defaults based on resolution
                 and GPU count. Good starting point for new datasets.
      stylegan2  Reproduce results for StyleGAN2 config F at 1024x1024.
      paper256   Reproduce results for FFHQ and LSUN Cat at 256x256.
      paper512   Reproduce results for BreCaHAD and AFHQ at 512x512.
      paper1024  Reproduce results for MetFaces at 1024x1024.
      cifar      Reproduce results for CIFAR-10 at 32x32.

    \b
    Transfer learning source networks (--resume):
      ffhq256        FFHQ trained at 256x256 resolution.
      ffhq512        FFHQ trained at 512x512 resolution.
      ffhq1024       FFHQ trained at 1024x1024 resolution.
      celebahq256    CelebA-HQ trained at 256x256 resolution.
      lsundog256     LSUN Dog trained at 256x256 resolution.
      <PATH or URL>  Custom network pickle.
    """
    dnnlib.util.Logger(should_flush=True)

    # Setup training options.
    try:
        run_desc, args = setup_training_loop_kwargs(**config_kwargs)
    except UserError as err:
        ctx.fail(err)

    # 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
    args.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{run_desc}')
    assert not os.path.exists(args.run_dir)

    # Print options.
    print()
    print('Training options:')
    print(json.dumps(args, indent=2))
    print()
    print(f'Output directory:   {args.run_dir}')
    print(f'Training data:      {args.training_set_kwargs.path}')
    print(f'Training duration:  {args.total_kimg} kimg')
    print(f'Number of GPUs:     {args.num_gpus}')
    print(f'Number of images:   {args.training_set_kwargs.max_size}')
    print(f'Image resolution:   {args.training_set_kwargs.resolution}')
    print(f'Conditional model:  {args.training_set_kwargs.use_labels}')
    print(f'Dataset x-flips:    {args.training_set_kwargs.xflip}')
    print()

    # Dry run?
    if dry_run:
        print('Dry run; exiting.')
        return

    # Create output directory.
    print('Creating output directory...')
    os.makedirs(args.run_dir)
    with open(os.path.join(args.run_dir, 'training_options.json'), 'wt') as f:
        json.dump(args, f, indent=2)

    # Launch processes.
    print('Launching processes...')
    torch.multiprocessing.set_start_method('spawn')
    with tempfile.TemporaryDirectory() as temp_dir:
        if args.num_gpus == 1:
            subprocess_fn(rank=0, args=args, temp_dir=temp_dir)
        else:
            torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus)


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

if __name__ == "__main__":
    main()  # pylint: disable=no-value-for-parameter

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