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train.py
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train.py
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/
"""Train diffusion-based generative model using the techniques described in the
paper "Elucidating the Design Space of Diffusion-Based Generative Models"."""
import os
import re
import json
import click
import torch
import dnnlib
from torch_utils import distributed as dist
from training import training_loop
import warnings
warnings.filterwarnings('ignore', 'Grad strides do not match bucket view strides') # False warning printed by PyTorch 1.12.
#----------------------------------------------------------------------------
# Parse a comma separated list of numbers or ranges and return a list of ints.
# Example: '1,2,5-10' returns [1, 2, 5, 6, 7, 8, 9, 10]
def parse_int_list(s):
if isinstance(s, list): return s
ranges = []
range_re = re.compile(r'^(\d+)-(\d+)$')
for p in s.split(','):
m = range_re.match(p)
if m:
ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
else:
ranges.append(int(p))
return ranges
#----------------------------------------------------------------------------
@click.command()
#Patch options
@click.option('--real_p', help='Full size image ratio', metavar='INT', type=click.FloatRange(min=0, max=1), default=0.5, show_default=True)
@click.option('--train_on_latents', help='Training on latent embeddings', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--progressive', help='Training on latent embeddings', metavar='BOOL', type=bool, default=False, show_default=True)
# Main options.
@click.option('--outdir', help='Where to save the results', metavar='DIR', type=str, required=True)
@click.option('--data', help='Path to the dataset', metavar='ZIP|DIR', type=str, required=True)
@click.option('--cond', help='Train class-conditional model', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--arch', help='Network architecture', metavar='ddpmpp|ncsnpp|adm', type=click.Choice(['ddpmpp', 'ncsnpp', 'adm']), default='ddpmpp', show_default=True)
@click.option('--precond', help='Preconditioning & loss function', metavar='vp|ve|edm', type=click.Choice(['vp', 've', 'edm', 'pedm']), default='pedm', show_default=True)
# Hyperparameters.
@click.option('--duration', help='Training duration', metavar='MIMG', type=click.FloatRange(min=0, min_open=True), default=200, show_default=True)
@click.option('--batch', help='Total batch size', metavar='INT', type=click.IntRange(min=1), default=512, show_default=True)
@click.option('--batch-gpu', help='Limit batch size per GPU', metavar='INT', type=click.IntRange(min=1))
@click.option('--cbase', help='Channel multiplier [default: varies]', metavar='INT', type=int)
@click.option('--cres', help='Channels per resolution [default: varies]', metavar='LIST', type=parse_int_list)
@click.option('--lr', help='Learning rate', metavar='FLOAT', type=click.FloatRange(min=0, min_open=True), default=10e-4, show_default=True)
@click.option('--ema', help='EMA half-life', metavar='MIMG', type=click.FloatRange(min=0), default=0.5, show_default=True)
@click.option('--dropout', help='Dropout probability', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.13, show_default=True)
@click.option('--augment', help='Augment probability', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.12, show_default=True)
@click.option('--xflip', help='Enable dataset x-flips', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--implicit_mlp', help='encoding coordbefore sending to the conv', metavar='BOOL', type=bool, default=False, show_default=True)
# Performance-related.
@click.option('--fp16', help='Enable mixed-precision training', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--ls', help='Loss scaling', metavar='FLOAT', type=click.FloatRange(min=0, min_open=True), default=1, show_default=True)
@click.option('--bench', help='Enable cuDNN benchmarking', metavar='BOOL', type=bool, default=True, show_default=True)
@click.option('--cache', help='Cache dataset in CPU memory', metavar='BOOL', type=bool, default=True, show_default=True)
@click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=1, show_default=True)
# I/O-related.
@click.option('--desc', help='String to include in result dir name', metavar='STR', type=str)
@click.option('--nosubdir', help='Do not create a subdirectory for results', is_flag=True)
@click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.IntRange(min=1), default=50, 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('--dump', help='How often to dump state', metavar='TICKS', type=click.IntRange(min=1), default=500, show_default=True)
@click.option('--seed', help='Random seed [default: random]', metavar='INT', type=int)
@click.option('--transfer', help='Transfer learning from network pickle', metavar='PKL|URL', type=str)
@click.option('--resume', help='Resume from previous training state', metavar='PT', type=str)
@click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True)
def main(**kwargs):
"""Train diffusion-based generative model using the techniques described in the
paper "Elucidating the Design Space of Diffusion-Based Generative Models".
Examples:
\b
# Train DDPM++ model for class-conditional CIFAR-10 using 8 GPUs
torchrun --standalone --nproc_per_node=8 train.py --outdir=training-runs \\
--data=datasets/cifar10-32x32.zip --cond=1 --arch=ddpmpp
"""
opts = dnnlib.EasyDict(kwargs)
torch.multiprocessing.set_start_method('spawn')
dist.init()
# Initialize config dict.
c = dnnlib.EasyDict()
c.dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=opts.data, use_labels=opts.cond, xflip=opts.xflip, cache=opts.cache)
c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, num_workers=opts.workers, prefetch_factor=2)
c.network_kwargs = dnnlib.EasyDict()
c.loss_kwargs = dnnlib.EasyDict()
c.optimizer_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=opts.lr, betas=[0.9,0.999], eps=1e-8)
c.real_p = opts.real_p
c.train_on_latents = opts.train_on_latents
c.progressive = opts.progressive
# Validate dataset options.
try:
dataset_obj = dnnlib.util.construct_class_by_name(**c.dataset_kwargs)
dataset_name = dataset_obj.name
c.dataset_kwargs.resolution = dataset_obj.resolution # be explicit about dataset resolution
c.dataset_kwargs.max_size = len(dataset_obj) # be explicit about dataset size
if opts.cond and not dataset_obj.has_labels:
raise click.ClickException('--cond=True requires labels specified in dataset.json')
del dataset_obj # conserve memory
except IOError as err:
raise click.ClickException(f'--data: {err}')
# Network architecture.
if opts.arch == 'ddpmpp':
c.network_kwargs.update(model_type='SongUNet', embedding_type='positional', encoder_type='standard', decoder_type='standard')
c.network_kwargs.update(channel_mult_noise=1, resample_filter=[1,1], model_channels=128, channel_mult=[2,2,2])
elif opts.arch == 'ncsnpp':
c.network_kwargs.update(model_type='SongUNet', embedding_type='fourier', encoder_type='residual', decoder_type='standard')
c.network_kwargs.update(channel_mult_noise=2, resample_filter=[1,3,3,1], model_channels=128, channel_mult=[2,2,2])
else:
assert opts.arch == 'adm'
c.network_kwargs.update(model_type='DhariwalUNet', model_channels=192, channel_mult=[1,2,3,4])
# Preconditioning & loss function.
if opts.precond == 'vp':
c.network_kwargs.class_name = 'training.networks.VPPrecond'
c.loss_kwargs.class_name = 'training.loss.VPLoss'
elif opts.precond == 've':
c.network_kwargs.class_name = 'training.networks.VEPrecond'
c.loss_kwargs.class_name = 'training.loss.VELoss'
elif opts.precond == 'pedm':
c.network_kwargs.class_name = 'training.networks.Patch_EDMPrecond'
c.loss_kwargs.class_name = 'training.patch_loss.Patch_EDMLoss'
else:
assert opts.precond == 'edm'
c.network_kwargs.class_name = 'training.networks.EDMPrecond'
c.loss_kwargs.class_name = 'training.loss.EDMLoss'
# Network options.
if opts.cbase is not None:
c.network_kwargs.model_channels = opts.cbase
if opts.cres is not None:
c.network_kwargs.channel_mult = opts.cres
if opts.augment:
c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', p=opts.augment)
c.augment_kwargs.update(xflip=1e8, yflip=1, scale=1, rotate_frac=1, aniso=1, translate_frac=1)
c.network_kwargs.augment_dim = 9
# c.augment_kwargs.update(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1)
# c.network_kwargs.augment_dim = 6
if opts.implicit_mlp:
c.network_kwargs.implicit_mlp = True
c.network_kwargs.update(dropout=opts.dropout, use_fp16=opts.fp16)
# Training options.
c.total_kimg = max(int(opts.duration * 1000), 1)
c.ema_halflife_kimg = int(opts.ema * 1000)
c.update(batch_size=opts.batch, batch_gpu=opts.batch_gpu)
c.update(loss_scaling=opts.ls, cudnn_benchmark=opts.bench)
c.update(kimg_per_tick=opts.tick, snapshot_ticks=opts.snap, state_dump_ticks=opts.dump)
# Random seed.
if opts.seed is not None:
c.seed = opts.seed
else:
seed = torch.randint(1 << 31, size=[], device=torch.device('cuda'))
torch.distributed.broadcast(seed, src=0)
c.seed = int(seed)
# Transfer learning and resume.
if opts.transfer is not None:
if opts.resume is not None:
raise click.ClickException('--transfer and --resume cannot be specified at the same time')
c.resume_pkl = opts.transfer
c.ema_rampup_ratio = None
elif opts.resume is not None:
match = re.fullmatch(r'training-state-(\d+).pt', os.path.basename(opts.resume))
if not match or not os.path.isfile(opts.resume):
raise click.ClickException('--resume must point to training-state-*.pt from a previous training run')
c.resume_pkl = os.path.join(os.path.dirname(opts.resume), f'network-snapshot-{match.group(1)}.pkl')
c.resume_kimg = int(match.group(1))
c.resume_state_dump = opts.resume
# Description string.
cond_str = 'cond' if c.dataset_kwargs.use_labels else 'uncond'
dtype_str = 'fp16' if c.network_kwargs.use_fp16 else 'fp32'
desc = f'{dataset_name:s}-{cond_str:s}-{opts.arch:s}-{opts.precond:s}-gpus{dist.get_world_size():d}-batch{c.batch_size:d}-{dtype_str:s}'
if opts.desc is not None:
desc += f'-{opts.desc}'
# Pick output directory.
if dist.get_rank() != 0:
c.run_dir = None
elif opts.nosubdir:
c.run_dir = opts.outdir
else:
prev_run_dirs = []
if os.path.isdir(opts.outdir):
prev_run_dirs = [x for x in os.listdir(opts.outdir) if os.path.isdir(os.path.join(opts.outdir, x))]
prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
cur_run_id = max(prev_run_ids, default=-1) + 1
c.run_dir = os.path.join(opts.outdir, f'{cur_run_id:05d}-{desc}')
assert not os.path.exists(c.run_dir)
# Print options.
dist.print0()
dist.print0('Training options:')
dist.print0(json.dumps(c, indent=2))
dist.print0()
dist.print0(f'Output directory: {c.run_dir}')
dist.print0(f'Dataset path: {c.dataset_kwargs.path}')
dist.print0(f'Class-conditional: {c.dataset_kwargs.use_labels}')
dist.print0(f'Network architecture: {opts.arch}')
dist.print0(f'Preconditioning & loss: {opts.precond}')
dist.print0(f'Number of GPUs: {dist.get_world_size()}')
dist.print0(f'Batch size: {c.batch_size}')
dist.print0(f'Mixed-precision: {c.network_kwargs.use_fp16}')
dist.print0()
# Dry run?
if opts.dry_run:
dist.print0('Dry run; exiting.')
return
# Create output directory.
dist.print0('Creating output directory...')
if dist.get_rank() == 0:
os.makedirs(c.run_dir, exist_ok=True)
with open(os.path.join(c.run_dir, 'training_options.json'), 'wt') as f:
json.dump(c, f, indent=2)
dnnlib.util.Logger(file_name=os.path.join(c.run_dir, 'log.txt'), file_mode='a', should_flush=True)
# Train.
training_loop.training_loop(**c)
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------