def main():
"""Main function."""
# Parse arguments.
args = parse_args()
# Parse configurations.
config = parse_config(args.config)
config = update_config(config, args.options)
config.work_dir = args.work_dir
config.checkpoint = args.checkpoint
config.launcher = args.launcher
config.backend = args.backend
if not os.path.isfile(config.checkpoint):
raise FileNotFoundError(f'Checkpoint file `{config.checkpoint}` is '
f'missing!')
# Set CUDNN.
config.cudnn_benchmark = config.get('cudnn_benchmark', True)
config.cudnn_deterministic = config.get('cudnn_deterministic', False)
torch.backends.cudnn.benchmark = config.cudnn_benchmark
torch.backends.cudnn.deterministic = config.cudnn_deterministic
# Setting for launcher.
config.is_distributed = True
init_dist(config.launcher, backend=config.backend)
config.num_gpus = dist.get_world_size()
# Setup logger.
if dist.get_rank() == 0:
logger_type = config.get('logger_type', 'normal')
logger = build_logger(logger_type, work_dir=config.work_dir)
shutil.copy(args.config, os.path.join(config.work_dir, 'config.py'))
commit_id = os.popen('git rev-parse HEAD').readline()
logger.info(f'Commit ID: {commit_id}')
else:
logger = build_logger('dumb', work_dir=config.work_dir)
# Start inference.
runner = getattr(runners, config.runner_type)(config, logger)
runner.load(filepath=config.checkpoint,
running_metadata=False,
learning_rate=False,
optimizer=False,
running_stats=False)
if args.synthesis_num > 0:
num = args.synthesis_num
logger.print()
logger.info(f'Synthesizing images ...')
runner.synthesize(num, html_name=f'synthesis_{num}.html')
logger.info(f'Finish synthesizing {num} images.')
if args.fid_num > 0:
num = args.fid_num
logger.print()
logger.info(f'Testing FID ...')
fid_value = runner.fid(num, align_tf=not args.use_torchvision)
logger.info(f'Finish testing FID on {num} samples. '
f'The result is {fid_value:.6f}.')
def main():
"""Main function."""
# Parse arguments.
args = parse_args()
# Parse configurations.
config = parse_config(args.config)
config = update_config(config, args.options)
os.environ['CUDA_VISIBLE_DEVICES'] = config.gpus
timestamp = datetime.datetime.now()
version = '%d-%d-%d-%02.0d-%02.0d-%02.0d' % \
(timestamp.year, timestamp.month, timestamp.day, timestamp.hour, timestamp.minute, timestamp.second)
work_dir = os.path.join(args.work_dir, version)
config.work_dir = work_dir
config.resume_path = args.resume_path
config.weight_path = args.weight_path
config.seed = args.seed
config.launcher = args.launcher
config.backend = args.backend
# Set CUDNN.
config.cudnn_benchmark = config.get('cudnn_benchmark', True)
config.cudnn_deterministic = config.get('cudnn_deterministic', False)
torch.backends.cudnn.benchmark = config.cudnn_benchmark
torch.backends.cudnn.deterministic = config.cudnn_deterministic
# Set random seed.
if config.seed is not None:
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
config.cudnn_deterministic = True
torch.backends.cudnn.deterministic = True
warnings.warn('Random seed is set for training! '
'This will turn on the CUDNN deterministic setting, '
'which may slow down the training considerably! '
'Unexpected behavior can be observed when resuming from '
'checkpoints.')
# Set launcher.
config.is_distributed = True
init_dist(config.launcher, backend=config.backend)
config.num_gpus = dist.get_world_size()
# Setup logger.
if dist.get_rank() == 0:
logger_type = config.get('logger_type', 'normal')
logger = build_logger(logger_type, work_dir=config.work_dir)
shutil.copy(args.config, os.path.join(config.work_dir, 'config.py'))
commit_id = os.popen('git rev-parse HEAD').readline()
logger.info(f'Commit ID: {commit_id}')
else:
logger = build_logger('dumb', work_dir=config.work_dir)
# Start training.
runner = getattr(runners, config.runner_type)(config, logger)
if config.resume_path:
runner.load(filepath=config.resume_path,
running_metadata=True,
learning_rate=True,
optimizer=True,
running_stats=False)
if config.weight_path:
runner.load(filepath=config.weight_path,
running_metadata=False,
learning_rate=False,
optimizer=False,
running_stats=False)
runner.train()
def main():
"""Main function."""
# Parse arguments.
args = parse_args()
# Parse configurations.
config = parse_config(args.config)
config = update_config(config, args.options)
config.work_dir = args.work_dir
config.resume_path = args.resume_path
config.weight_path = args.weight_path
config.seed = args.seed
config.launcher = args.launcher
config.backend = args.backend
if args.adv != None:
config.loss['g_loss_kwargs']['adv'] = float(args.adv)
if args.lamb != None:
config.loss['g_loss_kwargs']['lamb'] = float(args.lamb)
if args.metric != None:
config.loss['g_loss_kwargs']['metric'] = args.metric
if args.baseLR != None:
config.modules['generator']['opt']['base_lr'] = float(args.baseLR) / 2
if args.nethz != None:
config.nethz = args.nethz
config.savename = args.adv + '_' + args.lamb.replace(
'.', 'dot') + '_' + args.metric.replace(
'.', 'dot') + '_' + args.baseLR.replace('.', 'dot')
config.data['train'][
'root_dir'] = '/cluster/scratch/' + config.nethz + '/data'
config.data['val'][
'root_dir'] = '/cluster/scratch/' + config.nethz + '/data'
# Set CUDNN.
config.cudnn_benchmark = config.get('cudnn_benchmark', True)
config.cudnn_deterministic = config.get('cudnn_deterministic', False)
torch.backends.cudnn.benchmark = config.cudnn_benchmark
torch.backends.cudnn.deterministic = config.cudnn_deterministic
# Set random seed.
config.seed = 26
if config.seed is not None:
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
config.cudnn_deterministic = True
torch.backends.cudnn.deterministic = True
warnings.warn('Random seed is set for training! '
'This will turn on the CUDNN deterministic setting, '
'which may slow down the training considerably! '
'Unexpected behavior can be observed when resuming from '
'checkpoints.')
# Set launcher.
config.is_distributed = True
init_dist(config.launcher, backend=config.backend)
config.num_gpus = dist.get_world_size()
# Setup logger.
if dist.get_rank() == 0:
logger_type = config.get('logger_type', 'normal')
logger = build_logger(logger_type, work_dir=config.work_dir)
shutil.copy(args.config, os.path.join(config.work_dir, 'config.py'))
commit_id = os.popen('git rev-parse HEAD').readline()
logger.info(f'Commit ID: {commit_id}')
else:
logger = build_logger('dumb', work_dir=config.work_dir)
# Start training.
runner = getattr(runners, config.runner_type)(config, logger)
if config.resume_path:
runner.load(filepath=config.resume_path,
running_metadata=True,
learning_rate=True,
optimizer=True,
running_stats=False)
if config.weight_path:
runner.load(filepath=config.weight_path,
running_metadata=False,
learning_rate=False,
optimizer=False,
running_stats=False)
runner.train()