def main(argv):
args = argument_handler.train_arg_parser(argv)
args.lr, args.weight_decay = default_configs.optimisation_params(
'classification', args)
# FIXME: cant take more than one GPU
args.gpus = args.gpus[0]
if 'random_' in args.dataset:
args.dataset = args.dataset.replace('random_', '')
args.random_labels = True
else:
args.random_labels = False
# TODO: why load weights is False?
args.out_dir = prepare_training.prepare_output_directories(
dataset_name=args.dataset,
network_name=args.network_name,
optimiser='sgd',
load_weights=False,
experiment_name=args.experiment_name,
framework='pytorch')
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn(
'You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting from checkpoints.'
)
if args.gpus is not None:
warnings.warn(
'You have chosen a specific GPU. This will completely disable data parallelism.'
)
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
json_file_name = os.path.join(args.out_dir, 'args.json')
with open(json_file_name, 'w') as fp:
json.dump(dict(args._get_kwargs()), fp, sort_keys=True, indent=4)
ngpus_per_node = torch.cuda.device_count()
if args.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker,
nprocs=ngpus_per_node,
args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(ngpus_per_node, args)
def main(argv):
args = argument_handler.train_arg_parser(argv, extra_args_fun)
if args.prediction:
from kernelphysiology.dl.utils import augmentation
from kernelphysiology.dl.utils import arguments as ah
args.manipulation, args.parameters = ah.create_manipulation_list(
args.manipulation, args.parameters,
augmentation.get_testing_augmentations())
if args.lr is None:
args.lr = 0.1
if args.weight_decay is None:
args.weight_decay = 1e-4
# FIXME: cant take more than one GPU
args.gpus = args.gpus[0]
# TODO: why load weights is False?
args.out_dir = prepare_training.prepare_output_directories(
dataset_name=args.dataset,
network_name=args.network_name,
optimiser='sgd',
load_weights=False,
experiment_name=args.experiment_name,
framework='pytorch')
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn(
'You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting from checkpoints.'
)
if args.gpus is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()
if args.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker,
nprocs=ngpus_per_node,
args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(ngpus_per_node, args)
def main(argv):
args = argument_handler.train_arg_parser(argv, extra_args_fun)
args.lr, args.weight_decay = default_configs.optimisation_params(
'classification', args)
args.num_classes = 2
# FIXME: cant take more than one GPU
args.gpus = args.gpus[0]
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn(
'You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting from checkpoints.'
)
if args.gpus is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()
if args.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker,
nprocs=ngpus_per_node,
args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(ngpus_per_node, args)
