583f2bdd13
BOMs barf on UTF-8 decoding within PyTorch:
File "D:\Soft\Miniconda3\lib\site-packages\torch\utils_cpp_extension_versioner.py", line 16, in hash_source_files
hash_value = update_hash(hash_value, file.read())
UnicodeDecodeError: 'gbk' codec can't decode byte 0xbf in position 2: illegal multibyte sequence
Should fix #10, #14
288 lines
16 KiB
Python
288 lines
16 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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"""Train a GAN using the techniques described in the paper
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"Alias-Free Generative Adversarial Networks"."""
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import os
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import click
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import re
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import json
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import tempfile
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import torch
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import dnnlib
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from training import training_loop
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from metrics import metric_main
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from torch_utils import training_stats
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from torch_utils import custom_ops
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#----------------------------------------------------------------------------
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def subprocess_fn(rank, c, temp_dir):
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dnnlib.util.Logger(file_name=os.path.join(c.run_dir, 'log.txt'), file_mode='a', should_flush=True)
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# Init torch.distributed.
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if c.num_gpus > 1:
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init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
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if os.name == 'nt':
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init_method = 'file:///' + init_file.replace('\\', '/')
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torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus)
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else:
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init_method = f'file://{init_file}'
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torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus)
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# Init torch_utils.
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sync_device = torch.device('cuda', rank) if c.num_gpus > 1 else None
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training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
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if rank != 0:
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custom_ops.verbosity = 'none'
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# Execute training loop.
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training_loop.training_loop(rank=rank, **c)
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#----------------------------------------------------------------------------
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def launch_training(c, desc, outdir, dry_run):
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dnnlib.util.Logger(should_flush=True)
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# Pick output directory.
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prev_run_dirs = []
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if os.path.isdir(outdir):
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prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]
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prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
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prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
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cur_run_id = max(prev_run_ids, default=-1) + 1
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c.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}')
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assert not os.path.exists(c.run_dir)
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# Print options.
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print()
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print('Training options:')
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print(json.dumps(c, indent=2))
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print()
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print(f'Output directory: {c.run_dir}')
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print(f'Number of GPUs: {c.num_gpus}')
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print(f'Batch size: {c.batch_size} images')
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print(f'Training duration: {c.total_kimg} kimg')
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print(f'Dataset path: {c.training_set_kwargs.path}')
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print(f'Dataset size: {c.training_set_kwargs.max_size} images')
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print(f'Dataset resolution: {c.training_set_kwargs.resolution}')
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print(f'Dataset labels: {c.training_set_kwargs.use_labels}')
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print(f'Dataset x-flips: {c.training_set_kwargs.xflip}')
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print()
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# Dry run?
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if dry_run:
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print('Dry run; exiting.')
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return
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# Create output directory.
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print('Creating output directory...')
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os.makedirs(c.run_dir)
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with open(os.path.join(c.run_dir, 'training_options.json'), 'wt') as f:
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json.dump(c, f, indent=2)
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# Launch processes.
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print('Launching processes...')
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torch.multiprocessing.set_start_method('spawn')
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with tempfile.TemporaryDirectory() as temp_dir:
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if c.num_gpus == 1:
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subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
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else:
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torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus)
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#----------------------------------------------------------------------------
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def init_dataset_kwargs(data):
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try:
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dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False)
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dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) # Subclass of training.dataset.Dataset.
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dataset_kwargs.resolution = dataset_obj.resolution # Be explicit about resolution.
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dataset_kwargs.use_labels = dataset_obj.has_labels # Be explicit about labels.
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dataset_kwargs.max_size = len(dataset_obj) # Be explicit about dataset size.
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return dataset_kwargs, dataset_obj.name
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except IOError as err:
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raise click.ClickException(f'--data: {err}')
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#----------------------------------------------------------------------------
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def parse_comma_separated_list(s):
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if isinstance(s, list):
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return s
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if s is None or s.lower() == 'none' or s == '':
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return []
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return s.split(',')
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#----------------------------------------------------------------------------
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@click.command()
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# Required.
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@click.option('--outdir', help='Where to save the results', metavar='DIR', required=True)
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@click.option('--cfg', help='Base configuration', type=click.Choice(['stylegan3-t', 'stylegan3-r', 'stylegan2']), required=True)
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@click.option('--data', help='Training data', metavar='[ZIP|DIR]', type=str, required=True)
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@click.option('--gpus', help='Number of GPUs to use', metavar='INT', type=click.IntRange(min=1), required=True)
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@click.option('--batch', help='Total batch size', metavar='INT', type=click.IntRange(min=1), required=True)
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@click.option('--gamma', help='R1 regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), required=True)
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# Optional features.
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@click.option('--cond', help='Train conditional model', metavar='BOOL', type=bool, default=False, show_default=True)
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@click.option('--mirror', help='Enable dataset x-flips', metavar='BOOL', type=bool, default=False, show_default=True)
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@click.option('--aug', help='Augmentation mode', type=click.Choice(['noaug', 'ada', 'fixed']), default='ada', show_default=True)
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@click.option('--resume', help='Resume from given network pickle', metavar='[PATH|URL]', type=str)
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@click.option('--freezed', help='Freeze first layers of D', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
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# Misc hyperparameters.
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@click.option('--p', help='Probability for --aug=fixed', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.2, show_default=True)
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@click.option('--target', help='Target value for --aug=ada', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.6, show_default=True)
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@click.option('--batch-gpu', help='Limit batch size per GPU', metavar='INT', type=click.IntRange(min=1))
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@click.option('--cbase', help='Capacity multiplier', metavar='INT', type=click.IntRange(min=1), default=32768, show_default=True)
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@click.option('--cmax', help='Max. feature maps', metavar='INT', type=click.IntRange(min=1), default=512, show_default=True)
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@click.option('--glr', help='G learning rate [default: varies]', metavar='FLOAT', type=click.FloatRange(min=0))
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@click.option('--dlr', help='D learning rate', metavar='FLOAT', type=click.FloatRange(min=0), default=0.002, show_default=True)
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@click.option('--map-depth', help='Mapping network depth [default: varies]', metavar='INT', type=click.IntRange(min=1))
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@click.option('--mbstd-group', help='Minibatch std group size', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True)
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# Misc settings.
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@click.option('--desc', help='String to include in result dir name', metavar='STR', type=str)
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@click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True)
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@click.option('--kimg', help='Total training duration', metavar='KIMG', type=click.IntRange(min=1), default=25000, show_default=True)
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@click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.IntRange(min=1), default=4, show_default=True)
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@click.option('--snap', help='How often to save snapshots', metavar='TICKS', type=click.IntRange(min=1), default=50, show_default=True)
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@click.option('--seed', help='Random seed', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
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@click.option('--fp32', help='Disable mixed-precision', metavar='BOOL', type=bool, default=False, show_default=True)
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@click.option('--nobench', help='Disable cuDNN benchmarking', metavar='BOOL', type=bool, default=False, show_default=True)
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@click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=3, show_default=True)
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@click.option('-n','--dry-run', help='Print training options and exit', is_flag=True)
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def main(**kwargs):
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"""Train a GAN using the techniques described in the paper
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"Alias-Free Generative Adversarial Networks".
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Examples:
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\b
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# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
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python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \\
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--gpus=8 --batch=32 --gamma=8.2 --mirror=1
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\b
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# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
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python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \\
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--gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \\
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--resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
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\b
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# Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
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python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \\
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--gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug
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"""
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# Initialize config.
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opts = dnnlib.EasyDict(kwargs) # Command line arguments.
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c = dnnlib.EasyDict() # Main config dict.
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c.G_kwargs = dnnlib.EasyDict(class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict())
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c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict())
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c.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
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c.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
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c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss')
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c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2)
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# Training set.
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c.training_set_kwargs, dataset_name = init_dataset_kwargs(data=opts.data)
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if opts.cond and not c.training_set_kwargs.use_labels:
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raise click.ClickException('--cond=True requires labels specified in dataset.json')
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c.training_set_kwargs.use_labels = opts.cond
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c.training_set_kwargs.xflip = opts.mirror
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# Hyperparameters & settings.
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c.num_gpus = opts.gpus
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c.batch_size = opts.batch
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c.batch_gpu = opts.batch_gpu or opts.batch // opts.gpus
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c.G_kwargs.channel_base = c.D_kwargs.channel_base = opts.cbase
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c.G_kwargs.channel_max = c.D_kwargs.channel_max = opts.cmax
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c.G_kwargs.mapping_kwargs.num_layers = (8 if opts.cfg == 'stylegan2' else 2) if opts.map_depth is None else opts.map_depth
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c.D_kwargs.block_kwargs.freeze_layers = opts.freezed
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c.D_kwargs.epilogue_kwargs.mbstd_group_size = opts.mbstd_group
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c.loss_kwargs.r1_gamma = opts.gamma
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c.G_opt_kwargs.lr = (0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr
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c.D_opt_kwargs.lr = opts.dlr
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c.metrics = opts.metrics
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c.total_kimg = opts.kimg
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c.kimg_per_tick = opts.tick
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c.image_snapshot_ticks = c.network_snapshot_ticks = opts.snap
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c.random_seed = c.training_set_kwargs.random_seed = opts.seed
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c.data_loader_kwargs.num_workers = opts.workers
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# Sanity checks.
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if c.batch_size % c.num_gpus != 0:
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raise click.ClickException('--batch must be a multiple of --gpus')
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if c.batch_size % (c.num_gpus * c.batch_gpu) != 0:
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raise click.ClickException('--batch must be a multiple of --gpus times --batch-gpu')
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if c.batch_gpu < c.D_kwargs.epilogue_kwargs.mbstd_group_size:
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raise click.ClickException('--batch-gpu cannot be smaller than --mbstd')
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if any(not metric_main.is_valid_metric(metric) for metric in c.metrics):
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raise click.ClickException('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
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# Base configuration.
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c.ema_kimg = c.batch_size * 10 / 32
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if opts.cfg == 'stylegan2':
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c.G_kwargs.class_name = 'training.networks_stylegan2.Generator'
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c.loss_kwargs.style_mixing_prob = 0.9 # Enable style mixing regularization.
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c.loss_kwargs.pl_weight = 2 # Enable path length regularization.
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c.G_reg_interval = 4 # Enable lazy regularization for G.
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c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions.
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c.loss_kwargs.pl_no_weight_grad = True # Speed up path length regularization by skipping gradient computation wrt. conv2d weights.
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else:
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c.G_kwargs.class_name = 'training.networks_stylegan3.Generator'
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c.G_kwargs.magnitude_ema_beta = 0.5 ** (c.batch_size / (20 * 1e3))
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if opts.cfg == 'stylegan3-r':
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c.G_kwargs.conv_kernel = 1 # Use 1x1 convolutions.
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c.G_kwargs.channel_base *= 2 # Double the number of feature maps.
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c.G_kwargs.channel_max *= 2
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c.G_kwargs.use_radial_filters = True # Use radially symmetric downsampling filters.
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c.loss_kwargs.blur_init_sigma = 10 # Blur the images seen by the discriminator.
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c.loss_kwargs.blur_fade_kimg = c.batch_size * 200 / 32 # Fade out the blur during the first N kimg.
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# Augmentation.
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if opts.aug != 'noaug':
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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)
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if opts.aug == 'ada':
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c.ada_target = opts.target
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if opts.aug == 'fixed':
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c.augment_p = opts.p
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# Resume.
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if opts.resume is not None:
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c.resume_pkl = opts.resume
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c.ada_kimg = 100 # Make ADA react faster at the beginning.
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c.ema_rampup = None # Disable EMA rampup.
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c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup.
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# Performance-related toggles.
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if opts.fp32:
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c.G_kwargs.num_fp16_res = c.D_kwargs.num_fp16_res = 0
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c.G_kwargs.conv_clamp = c.D_kwargs.conv_clamp = None
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if opts.nobench:
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c.cudnn_benchmark = False
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# Description string.
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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}'
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if opts.desc is not None:
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desc += f'-{opts.desc}'
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# Launch.
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launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
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#----------------------------------------------------------------------------
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if __name__ == "__main__":
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main() # pylint: disable=no-value-for-parameter
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#----------------------------------------------------------------------------
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