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train.py
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train.py
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# 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.
# modified for "Ensembling Off-the-shelf Models for GAN Training"
import os
import click
import re
import json
import tempfile
import torch
import dnnlib
import argparse
import importlib
from training import training_loop
from metrics import metric_main
from torch_utils import training_stats
from torch_utils import custom_ops
#----------------------------------------------------------------------------
class UserError(Exception):
pass
#----------------------------------------------------------------------------
def setup_training_loop_kwargs(
# General options (not included in desc).
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'
gamma = None, # Override R1 gamma: <float>
pl_weight = None, # Override R1 gamma: <float>
g_reg_interval = None, # Override R1 gamma: <float>
d_reg_interval = None, # Override R1 gamma: <float>
kimg = None, # Override training duration: <int>
batch = None, # Override batch size: <int>
batch_gpu = None, # Override batch size per gpu: <int>
# Discriminator augmentation.
diffaugment= None, # Comma-separated list of DiffAugment policy, default = 'color,translation,cutout'
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
augpipe = None, # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc'
# 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
# Visison-aided-gan training configs.
cv = None, # cv supervision on images
warmup = False, # warmup if training from scratch
cv_loss = None, # cv loss
augcv = None, # Augmentation mode for CV input: 'ada' (default), 'noaug', 'fixed'
augpipecv = None, # Augmentation pipeline for CV:
ada_target_cv = None, #target augmentation p for CV model
# miscellaneous
wandb_log = None, # use wandb logging: setup entity and project name in training/trainin_loop.py
exact_resume = 0, # if 1 resume augmentation pipeline and optimizer
clean = None, # FID clean or not
**kwargs,
):
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 = 25
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 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 = {
'auto': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, fmaps=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=2), # Populated dynamically based on resolution and GPU count.
'stylegan2': dict(ref_gpus=4, kimg=25000, mb=16, mbstd=4, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8), # Uses mixed-precision, unlike the original StyleGAN2.
'paper256': dict(ref_gpus=2, kimg=25000, mb=16, mbstd=4, fmaps=0.5, lrate=0.002, gamma=1, ema=10, ramp=None, map=8),
'paper512': dict(ref_gpus=4, kimg=25000, mb=16, mbstd=4, fmaps=1, lrate=0.0025, gamma=0.5, ema=20, ramp=None, map=8),
'paper1024': dict(ref_gpus=4, kimg=25000, mb=16, mbstd=4, fmaps=1, lrate=0.002, gamma=2, ema=10, ramp=None, map=8),
'cifar': dict(ref_gpus=2, kimg=100000, mb=64, mbstd=32, fmaps=1, lrate=0.0025, gamma=0.01, ema=500, ramp=0.05, map=2),
'paper256_2fmap': dict(ref_gpus=2, kimg=25000, mb=16, mbstd=4, fmaps=1, lrate=0.002, gamma=1, ema=10, ramp=None, map=8),
}
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
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.lrate, betas=[0,0.99], eps=1e-8)
args.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, 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 pl_weight is not None:
args.loss_kwargs.pl_weight = pl_weight
if g_reg_interval == 0:
args.G_reg_interval = None
if d_reg_interval ==0:
args.D_reg_interval = None
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_gpu or batch // gpus
# ---------------------------------------------------
# Discriminator augmentation: aug, p, target, augpipe
# ---------------------------------------------------
if diffaugment:
args.loss_kwargs.diffaugment = diffaugment
aug = 'noaug'
desc += '-{}'.format(diffaugment.replace(',', '-'))
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])
# ----------------------------------
# Vision-aided-Adversarial loss
# ----------------------------------
args.cvD_kwargs = None
args.warmup = warmup
if cv is not None:
desc += '-{}'.format(cv)
class_name = 'vision_model.cvmodel.CVWrapper'
cv_specs={'cv_type': cv}
args.cv_kwargs = dnnlib.EasyDict(class_name=class_name, **cv_specs)
args.cv_loss = cv_loss
desc += '-cv_loss_{}'.format(cv_loss)
args.cvD_kwargs = dnnlib.EasyDict(class_name='training.cv_discriminator.Discriminator', cv_type= cv )
args.cvD_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0,0.99], eps=1e-8)
# ----------------------------------
# Pretrained model based Discriminator augmentation: augcv, p, target, augpipecv
# ----------------------------------
if augcv is not None:
if 'noaug' in augcv:
args.augment_kwargs_cv = None
elif 'diffaug' in augcv:
args.loss_kwargs.augment_pipe_cv = augcv
args.augment_kwargs_cv = None
desc += '-augcv_diffaug'
elif augcv =='ada':
args.ada_target_cv = ada_target_cv
desc += f'-augcv_{augcv}'
if augpipecv is None:
augpipecv = 'bgc'
args.augment_kwargs_cv = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', **augpipe_specs[augpipecv])
# ----------------------------------
# Transfer learning: resume, freezed
# ----------------------------------
resume_specs = {
'ffhq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl',
'ffhq512': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl',
'ffhq1024': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl',
'celebahq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl',
'lsundog256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl',
}
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':
if cv is None:
args.ada_kimg = 100 # make ADA react faster at the beginning. We remove this when we add our loss on a model trained with standard adversarial loss till some iterations
args.ema_rampup = None # disable EMA rampup
args.exact_resume = exact_resume
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
args.wandb_log = wandb_log
args.clean = clean
args.desc = desc
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.
importlib.reload(training_stats)
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 convert(self, value, param, ctx):
_ = param, ctx
if value is None or value.lower() == 'none' or value == '':
return []
return value.split(',')
#----------------------------------------------------------------------------
def get_args_parser():
parser = argparse.ArgumentParser('main')
# General options.
parser.add_argument('--outdir', help='Where to save the results', required=True, metavar='DIR')
parser.add_argument('--gpus', help='Number of GPUs to use [default: 1]', type=int, metavar='INT')
parser.add_argument('--snap', help='Snapshot interval [default: 50 ticks]', type=int, metavar='INT')
parser.add_argument('--metrics', help='Comma-separated list or "none" [default: fid50k_full]', type=CommaSeparatedList())
parser.add_argument('--seed', help='Random seed [default: 0]', type=int, metavar='INT')
parser.add_argument('--dry-run', help='Print training options and exit', action='store_true', default=False)
# Dataset.
parser.add_argument('--data', help='Training data (directory or zip)', metavar='PATH', required=True)
parser.add_argument('--cond', help='Train conditional model based on dataset labels [default: false]', type=bool, metavar='BOOL')
parser.add_argument('--subset', help='Train with only N images [default: all]', type=int, metavar='INT')
parser.add_argument('--mirror', help='Enable dataset x-flips [default: false]', type=bool, metavar='BOOL')
# Base config.
parser.add_argument('--cfg', help='Base config [default: auto]', type=click.Choice(['auto', 'stylegan2', 'paper256', 'paper256_2fmap', 'paper512', 'paper1024', 'cifar']))
parser.add_argument('--gamma', help='Override R1 gamma', type=float)
parser.add_argument('--pl-weight', help='Override G path regularization', type=float)
parser.add_argument('--g-reg-interval', help='lazy regularization G interval', type=int, metavar='INT')
parser.add_argument('--d-reg-interval', help='lazy regularization D interval', type=int, metavar='INT')
parser.add_argument('--kimg', help='Override training duration', type=int, metavar='INT')
parser.add_argument('--batch', help='Override batch size', type=int, metavar='INT')
parser.add_argument('--batch-gpu', help='Override batch size per gpu', type=int, metavar='INT')
# Discriminator augmentation.
parser.add_argument('--diffaugment', help='Comma-separated list of DiffAugment policy [default: color,translation,cutout]', type=str)
parser.add_argument('--aug', help='Augmentation mode [default: ada]', type=click.Choice(['noaug', 'ada', 'fixed']))
parser.add_argument('--p', help='Augmentation probability for --aug=fixed', type=float)
parser.add_argument('--target', help='ADA target value for --aug=ada', type=float)
parser.add_argument('--augpipe', help='Augmentation pipeline [default: bgc]', type=click.Choice(['blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc', 'bgcf', 'bgcfn', 'bgcfnc']))
# Transfer learning.
parser.add_argument('--resume', help='Resume training [default: noresume]', metavar='PKL')
parser.add_argument('--freezed', help='Freeze-D [default: 0 layers]', type=int, metavar='INT')
# Performance options.
parser.add_argument('--fp32', help='Disable mixed-precision training', type=bool, metavar='BOOL')
parser.add_argument('--nhwc', help='Use NHWC memory format with FP16', type=bool, metavar='BOOL')
parser.add_argument('--nobench', help='Disable cuDNN benchmarking', type=bool, metavar='BOOL')
parser.add_argument('--allow-tf32', help='Allow PyTorch to use TF32 internally', type=bool, metavar='BOOL')
parser.add_argument('--workers', help='Override number of DataLoader workers', type=int, metavar='INT')
#Vision-aided adversarial loss options
parser.add_argument('--cv', help='CV model [default: None]', type=str)
parser.add_argument('--warmup', help='if training from scratch, train with standard adversarial loss for 500k images and then introduce vision-aided-loss', type=bool, metavar='BOOL', default=False)
parser.add_argument('--cv-loss', default= '', help='CV loss', type=str)
parser.add_argument('--augcv', help='Augmentation mode [default: None]', type=str)
parser.add_argument('--augpipecv', default='bgc', help='Augmentation pipeline [default: bgc]', type=str)
parser.add_argument('--ada-target-cv', default=0.3, help='Augmentation target probability', type=float)
parser.add_argument('--exact-resume', help='0: only resume model weights, 1: resume model weights ensuring exact match and augpipe, 2: resume model weights, optimizer and augpipe', type=int, default=0, metavar='INT')
# miscellaneous
parser.add_argument('--wandb-log', help='wandb logging instead of tensorboard logging', type=bool, metavar='BOOL', default=False)
parser.add_argument('--clean', help='FID evaluation using clean-fid (https://github.com/GaParmar/clean-fid)', type=bool, metavar='BOOL', default=False)
return parser
def main(args):
"""Train a GAN using the techniques described in the paper
"Ensembling Off-the-shelf Models for GAN Training".
Examples:
\b
# Train StyleGAN2 on custom dataset using 1 GPU.
python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1
\b
Base configs (--cfg):
auto Automatically select reasonable defaults based on resolution
and GPU count. Good starting point for new datasets.
paper256 Reproduce results for 256 resolution dataset
paper256_2fmap Reproduce results for 256 resolution dataset with 2x feature channel in G.
paper512 Reproduce results for 512 resolution dataset
paper1024 Reproduce results for MetFaces at 1024x1024.
stylegan2 Reproduce results for StyleGAN2 config F at 1024x1024.
\b
Transfer learning source networks (--resume):
ffhq512 FFHQ trained at 512x512 resolution.
ffhq1024 FFHQ trained at 1024x1024 resolution.
<PATH or URL> Custom network pickle.
\b
Computer vision pretrained networks (--cv):
input-<model_name>-output-<output_type>
model_name: clip, dino, seg_ade, det_coco, face_seg, face_normals, vgg, swin
output_type: conv_multi_level for training (discriminator head is over the spatial features of pretrained model)
output_type: pool for model selection (to enable linear classifier on the feature vector)
for multiple model training: input is - separated models, i.e. clip-dino-swin; output is conv_multi_level_list<i> i = number of cv models.
\b
Loss function for computer vision model bsaed discriminator (--cv-loss):
sigmoid: standard loss used for all models except CLIP and DINO
multilevel: multi-level loss used in CLIP and DINO
sigmoid_s: sidmoid loss with one sided label smoothing
multilevel_s: multi level loss with one sided label smoothing
for multiple model training: loss is comma separated names, i.e. multilevel,sigmoid,sigmoid
\b
Augmentation for computer vision model bsaed discriminator (--augcv):
can be either "ada" or "diffaug-policy" where policy is a comma separated augmentation list
\b
Augmentation for original discriminator (--augcv, --diffaugment):
--augcv ada: For ADA augmentation
--diffaugment <policy>: For DiffAugment, where example policy is 'color,translation,cutout'
"""
dnnlib.util.Logger(should_flush=True)
outdir = args.outdir
dry_run = args.dry_run
# Setup training options.
try:
run_desc, args = setup_training_loop_kwargs(**vars(args))
except UserError as err:
print(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}')
args.desc = 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...')
try:
torch.multiprocessing.set_start_method('spawn')
except RuntimeError:
pass
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__":
args = get_args_parser()
main(args.parse_args())
#----------------------------------------------------------------------------