# 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. # # modified by Axel Sauer for "Projected GANs Converge Faster" # import os import click import re import json import tempfile import torch import legacy import dnnlib from training import training_loop from metrics import metric_main from torch_utils import training_stats from torch_utils import custom_ops from torch_utils import misc 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))] matching_dirs = [re.fullmatch(r'\d{5}' + f'-{desc}', x) for x in prev_run_dirs if re.fullmatch(r'\d{5}' + f'-{desc}', x) is not None] if c.restart_every > 0 and len(matching_dirs) > 0: # expect unique desc, continue in this directory assert len(matching_dirs) == 1, f'Multiple directories found for resuming: {matching_dirs}' c.run_dir = os.path.join(outdir, matching_dirs[0].group()) else: # fallback to standard 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, exist_ok=c.restart_every > 0) 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(['fastgan', 'fastgan_lite', '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) # Diffusion config. (Most diffusion settings are fixed in the projector.py file) @click.option('--target', help='Discriminator target', metavar='FLOAT', type=float, default=0.6, required=True) @click.option('--d_pos', help='Diffusion adding position', metavar='STR', type=str, default='first') @click.option('--noise_sd', help='Diffusion noise standard deviation', metavar='FLOAT', type=float, default=0.5) @click.option('--ada_kimg', help='# kimgs needed to push diffusion to maximum level', type=int, default=100) # 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=True, show_default=True) @click.option('--resume', help='Resume from given network pickle', metavar='[PATH|URL]', type=str) # Misc hyperparameters. @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), default=0.0002, show_default=True) @click.option('--dlr', help='D learning rate', metavar='FLOAT', type=click.FloatRange(min=0), default=0.0002, show_default=True) @click.option('--map-depth', help='Mapping network depth [default: varies]', metavar='INT', type=click.IntRange(min=1)) # 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) @click.option('--restart_every',help='Time interval in seconds to restart code', metavar='INT', type=int, default=9999999, show_default=True) def main(**kwargs): # 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=64, w_dim=128, mapping_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.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2) c.target = opts.target c.ada_kimg = opts.ada_kimg # 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 = opts.cbase c.G_kwargs.channel_max = opts.cmax c.G_kwargs.mapping_kwargs.num_layers = 2 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 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_opt_kwargs.lr = c.D_opt_kwargs.lr = 0.002 if c.training_set_kwargs.resolution >= 1024 else 0.0025 c.G_kwargs.class_name = 'pg_modules.networks_stylegan2.Generator' c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions. use_separable_discs = True elif opts.cfg in ['fastgan', 'fastgan_lite']: c.G_kwargs = dnnlib.EasyDict(class_name='pg_modules.networks_fastgan.Generator', cond=opts.cond, synthesis_kwargs=dnnlib.EasyDict()) c.G_kwargs.synthesis_kwargs.lite = (opts.cfg == 'fastgan_lite') c.G_opt_kwargs.lr = c.D_opt_kwargs.lr = 0.0002 use_separable_discs = False # Resume. if opts.resume is not None: c.resume_pkl = opts.resume c.ema_rampup = None # Disable EMA rampup. # Restart. c.restart_every = opts.restart_every # Performance-related toggles. if opts.fp32: c.G_kwargs.num_fp16_res = 0 c.G_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}-d_pos-{opts.d_pos}-noise_sd-{opts.noise_sd}' if opts.d_pos: desc += f"-target{opts.target}" if opts.ada_kimg: desc += f'-ada_kimg{opts.ada_kimg}' if opts.desc is not None: desc += f'-{opts.desc}' # Projected and Multi-Scale Discriminators c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.ProjectedGANLoss') c.D_kwargs = dnnlib.EasyDict( class_name='pg_modules.discriminator.ProjectedDiscriminator', diffaug=True, interp224=(c.training_set_kwargs.resolution < 224), backbone_kwargs=dnnlib.EasyDict(), ) c.D_kwargs.backbone_kwargs.d_pos = opts.d_pos # where to use curriculum diffused augmentation c.D_kwargs.backbone_kwargs.noise_sd = opts.noise_sd c.D_kwargs.backbone_kwargs.cout = 64 c.D_kwargs.backbone_kwargs.expand = True c.D_kwargs.backbone_kwargs.proj_type = 2 c.D_kwargs.backbone_kwargs.num_discs = 4 c.D_kwargs.backbone_kwargs.separable = use_separable_discs c.D_kwargs.backbone_kwargs.cond = opts.cond # Launch. launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run) # Check for restart last_snapshot = misc.get_ckpt_path(c.run_dir) if os.path.isfile(last_snapshot): # get current number of training images with dnnlib.util.open_url(last_snapshot) as f: cur_nimg = legacy.load_network_pkl(f)['progress']['cur_nimg'].item() if (cur_nimg//1000) < c.total_kimg: print('Restart: exit with code 3') exit(3) if __name__ == "__main__": main() # pylint: disable=no-value-for-parameter