def test_flatten_dict():
"""Validate flatten_dict can handle nested dictionaries and argparse Namespace."""
# Test basic dict flattening with custom delimiter
params = {"a": {"b": "c"}}
params = _flatten_dict(params, "--")
assert "a" not in params
assert params["a--b"] == "c"
# Test complex nested dict flattening
params = {
"a": {
5: {
"foo": "bar"
}
},
"b": 6,
"c": {
7: [1, 2, 3, 4],
8: "foo",
9: {
10: "bar"
}
}
}
params = _flatten_dict(params)
assert "a" not in params
assert params["a/5/foo"] == "bar"
assert params["b"] == 6
assert params["c/7"] == [1, 2, 3, 4]
assert params["c/8"] == "foo"
assert params["c/9/10"] == "bar"
# Test flattening of argparse Namespace
opt = "--max_epochs 1".split(" ")
parser = ArgumentParser()
parser = Trainer.add_argparse_args(parent_parser=parser)
params = parser.parse_args(opt)
wrapping_dict = {"params": params}
params = _flatten_dict(wrapping_dict)
assert type(params) == dict
assert params["params/logger"] is True
assert params["params/gpus"] == "None"
assert "logger" not in params
assert "gpus" not in params
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
params = _convert_params(params)
params = _flatten_dict(params)
self.experiment.argparse(Namespace(**params))
def test_sanitize_callable_params():
"""Callback function are not serializiable.
Therefore, we get them a chance to return something and if the returned type is not accepted, return None.
"""
opt = "--max_epochs 1".split(" ")
parser = ArgumentParser()
parser = Trainer.add_argparse_args(parent_parser=parser)
params = parser.parse_args(opt)
def return_something():
return "something"
params.something = return_something
def wrapper_something():
return return_something
params.wrapper_something_wo_name = lambda: lambda: "1"
params.wrapper_something = wrapper_something
params = _convert_params(params)
params = _flatten_dict(params)
params = _sanitize_callable_params(params)
assert params["gpus"] == "None"
assert params["something"] == "something"
assert params["wrapper_something"] == "wrapper_something"
assert params["wrapper_something_wo_name"] == "<lambda>"
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
params = _convert_params(params)
params = _flatten_dict(params)
for k, v in params.items():
if len(str(v)) > 250:
rank_zero_warn(
f"Mlflow only allows parameters with up to 250 characters. Discard {k}={v}",
category=RuntimeWarning)
continue
self.experiment.log_param(self.run_id, k, v)
def log_hyperparams(self,
params: Union[Dict[str, Any], Namespace],
metrics: Optional[Dict[str, Any]] = None) -> None:
"""Record hyperparameters. TensorBoard logs with and without saved hyperparameters are incompatible, the
hyperparameters are then not displayed in the TensorBoard. Please delete or move the previously saved logs
to display the new ones with hyperparameters.
Args:
params: a dictionary-like container with the hyperparameters
metrics: Dictionary with metric names as keys and measured quantities as values
"""
params = _convert_params(params)
# store params to output
if _OMEGACONF_AVAILABLE and isinstance(params, Container):
self.hparams = OmegaConf.merge(self.hparams, params)
else:
self.hparams.update(params)
# format params into the suitable for tensorboard
params = _flatten_dict(params)
params = self._sanitize_params(params)
if metrics is None:
if self._default_hp_metric:
metrics = {"hp_metric": -1}
elif not isinstance(metrics, dict):
metrics = {"hp_metric": metrics}
if metrics:
self.log_metrics(metrics, 0)
exp, ssi, sei = hparams(params, metrics)
writer = self.experiment._get_file_writer()
writer.add_summary(exp)
writer.add_summary(ssi)
writer.add_summary(sei)
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
params = _convert_params(params)
params = _flatten_dict(params)
params = _sanitize_callable_params(params)
self.experiment.config.update(params, allow_val_change=True)
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = _convert_params(params)
params = _flatten_dict(params)
self.experiment.log_parameters(params)
