def parse_args(parser):
args, _ = parser.parse_known_args()
assert args.pipeline, 'set pipeline module'
pipeline = get_module(args.pipeline)()
pipeline.export_args(parser)
# override defaults
if args.config:
with open(args.config) as f:
config = yaml.load(f)
parser.set_defaults(**config)
return parser.parse_args(), parser.parse_args([])
def load_pipeline(checkpoint, args_to_update=None):
ckpt = torch.load(checkpoint, map_location='cpu')
assert 'args' in ckpt
if args_to_update:
ckpt['args'].update(args_to_update)
try:
args = munch.munchify(ckpt['args'])
pipeline = get_module(args.pipeline)()
pipeline.create(args)
except AttributeError as err:
print('\nERROR: Checkpoint args is incompatible with this version\n',
file=sys.stderr)
raise err
load_model_checkpoint(checkpoint, pipeline.get_net())
return pipeline, args
args.ignore_changed_args += ['config', 'pipeline']
exper_name = get_experiment_name(args, default_args, args.ignore_changed_args)
exper_dir = make_experiment_dir(args.save_dir)
writer = SummaryWriter(log_dir=exper_dir, flush_secs=10)
iter_cb = TrainIterCb(args, writer)
setup_logging(exper_dir)
print(f'experiment dir: {exper_dir}')
print_args(args, default_args)
args = eval_args(args)
pipeline = get_module(args.pipeline)()
pipeline.create(args)
required_attributes = ['model', 'ds_train', 'ds_val', 'optimizer', 'criterion']
check_pipeline_attributes(pipeline, required_attributes)
# lr_scheduler = [torch.optim.lr_scheduler.ReduceLROnPlateau(o, patience=3, factor=0.5, verbose=True) for o in pipeline.optimizer]
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(pipeline.optimizer, patience=3, factor=0.5, verbose=True)
if args.net_ckpt:
print(f'LOAD NET CHECKPOINT {args.net_ckpt}')
load_model_checkpoint(args.net_ckpt, pipeline.get_net())
if hasattr(pipeline.model, 'ray_block') and pipeline.model.ray_block is not None:
if hasattr(args, 'ray_block_ckpt') and args.ray_block_ckpt:
print(f'LOAD RAY BLOCK CHECKPOINT {args.ray_block_ckpt}')
