def build_optimizer_and_scheduler(
conf: OmegaConf, model: torch.nn.Module) -> Tuple[Optimizer, Any]:
conf = OmegaConf.to_container(conf, resolve=True)
optimizer_fn = import_(conf.pop("name"))
lr = conf.pop("lr") if "lr" in conf else 1e-3
optimizer = optimizer_fn(
[
{
"params": model.backbone.parameters(),
"lr": lr * 0.1
},
{
"params": model.classifier.parameters(),
"lr": lr
},
],
**conf,
)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="min",
patience=2)
return optimizer, scheduler
def process_config(cfg: OmegaConf):
if 'name' not in cfg or cfg.name is None:
raise ValueError(
"`cfg.name` must be provided to save a model checkpoint")
if 'checkpoint_paths' not in cfg or cfg.checkpoint_paths is None:
raise ValueError(
"`cfg.checkpoint_paths` must be provided as a list of one or more str paths to "
"pytorch lightning checkpoints")
save_ckpt_only = False
with open_dict(cfg):
name_prefix = cfg.name
checkpoint_paths = cfg.pop('checkpoint_paths')
if 'save_ckpt_only' in cfg:
save_ckpt_only = cfg.pop('save_ckpt_only')
if type(checkpoint_paths) not in (list, tuple):
checkpoint_paths = str(checkpoint_paths).replace("[",
"").replace("]", "")
checkpoint_paths = checkpoint_paths.split(",")
checkpoint_paths = [
ckpt_path.strip() for ckpt_path in checkpoint_paths
]
return name_prefix, checkpoint_paths, save_ckpt_only
def _convert_config(cfg: OmegaConf):
""" Recursive function convertint the configuration from old hydra format to the new one. """
# Get rid of cls -> _target_.
if 'cls' in cfg and "_target_" not in cfg:
cfg._target_ = cfg.pop("cls")
# Get rid of params.
if 'params' in cfg:
params = cfg.pop('params')
for param_key, param_val in params.items():
cfg[param_key] = param_val
# Recursion.
try:
for _, sub_cfg in cfg.items():
if isinstance(sub_cfg, DictConfig):
_convert_config(sub_cfg)
except omegaconf_errors.OmegaConfBaseException as e:
logging.warning(f"Skipping config conversion for cfg:\n{cfg}\n due to OmegaConf error encountered :\n{e}.")
def build_optimizer_and_scheduler(
conf: OmegaConf, model: torch.nn.Module) -> Tuple[Optimizer, Any]:
conf = OmegaConf.to_container(conf, resolve=True)
optimizer_fn = import_(conf.pop("name"))
optimizer = optimizer_fn(model.parameters(), **conf)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="min",
patience=2)
return optimizer, scheduler
