def parse_arguments(self, arg_list):
"""A version of speechbrain.parse_arguments enhanced for hyperparameter optimization.
If a parameter named 'hpopt' is provided, hyperparameter
optimization and reporting will be enabled.
If the parameter value corresponds to a filename, it will
be read as a hyperpyaml file, and the contents will be added
to "overrides". This is useful for cases where the values of
certain hyperparameters are different during hyperparameter
optimization vs during full training (e.g. number of epochs, saving
files, etc)
Arguments
---------
arg_list: a list of arguments
Returns
-------
param_file : str
The location of the parameters file.
run_opts : dict
Run options, such as distributed, device, etc.
overrides : dict
The overrides to pass to ``load_hyperpyyaml``.
Example
-------
>>> ctx = HyperparameterOptimizationContext()
>>> arg_list = ["hparams.yaml", "--x", "1", "--y", "2"]
>>> hparams_file, run_opts, overrides = ctx.parse_arguments(arg_list)
>>> print(f"File: {hparams_file}, Overrides: {overrides}")
File: hparams.yaml, Overrides: {'x': 1, 'y': 2}
"""
hparams_file, run_opts, overrides_yaml = sb.parse_arguments(arg_list)
overrides = load_hyperpyyaml(overrides_yaml)
hpopt = overrides.get(KEY_HPOPT, False)
hpopt_mode = overrides.get(KEY_HPOPT_MODE) or DEFAULT_REPORTER
if hpopt:
self.enabled = True
self.reporter = get_reporter(hpopt_mode, *self.reporter_args,
**self.reporter_kwargs)
if isinstance(hpopt, str) and os.path.exists(hpopt):
with open(hpopt) as hpopt_file:
trial_id = get_trial_id()
hpopt_overrides = load_hyperpyyaml(
hpopt_file,
overrides={"trial_id": trial_id},
overrides_must_match=False,
)
overrides = dict(hpopt_overrides, **overrides)
for key in [KEY_HPOPT, KEY_HPOPT_MODE]:
if key in overrides:
del overrides[key]
return hparams_file, run_opts, overrides
yield tokens
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
# 4. Set output:
sb.dataio.dataset.set_output_keys(
datasets,
["id", "sig", "semantics", "tokens_bos", "tokens_eos", "tokens"],
)
return train_data, valid_data, test_data, tokenizer
if __name__ == "__main__":
# Load hyperparameters file with command-line overrides
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
with open(hparams_file) as fin:
hparams = load_hyperpyyaml(fin, overrides)
show_results_every = 100 # plots results every N iterations
# If distributed_launch=True then
# create ddp_group with the right communication protocol
sb.utils.distributed.ddp_init_group(run_opts)
# Create experiment directory
sb.create_experiment_directory(
experiment_directory=hparams["output_folder"],
hyperparams_to_save=hparams_file,
overrides=overrides,
)
