def get_storage_dir():
# Sacred should not add path_template to the config
# -> move this few lines to a function
path_template = Path(os.environ["STORAGE"]) / "pth_evaluate" / nickname
return str(get_new_folder(
path_template, mkdir=False, consider_mpi=True,
))
def config():
debug = False
batch_size = 6
train_dataset = "mix_2_spk_min_tr"
validate_dataset = "mix_2_spk_min_cv"
# Start with an empty dict to allow tracking by Sacred
trainer = {
"model": {
"factory": pt.models.bss.PermutationInvariantTrainingModel,
"dropout_input": 0.,
"dropout_hidden": 0.,
"dropout_linear": 0.
},
"storage_dir": None,
"optimizer": {
"factory": pt.optimizer.Adam,
"gradient_clipping": 1
},
"summary_trigger": (1000, "iteration"),
"stop_trigger": (300_000, "iteration"),
"loss_weights": {
"pit_ips_loss": 1.0,
"pit_mse_loss": 0.0,
}
}
pt.Trainer.get_config(trainer)
if trainer['storage_dir'] is None:
trainer['storage_dir'] = get_new_folder(path_template, mkdir=False)
ex.observers.append(
FileStorageObserver.create(Path(trainer['storage_dir']) / 'sacred'))
def config():
debug = False
model_path = ''
assert len(model_path) > 0, 'Set the model path on the command line.'
checkpoint_name = 'ckpt_best_loss.pth'
experiment_dir = str(
get_new_folder(
path_template,
mkdir=False,
consider_mpi=True,
))
batch_size = 1
datasets = ["mix_2_spk_min_cv", "mix_2_spk_min_tt"]
locals() # Fix highlighting
def config():
debug = False
database_json = ''
if "WSJ0_2MIX" in os.environ:
database_json = os.environ.get("WSJ0_2MIX")
assert len(
database_json
) > 0, 'Set path to database Json on the command line or set environment variable WSJ0_2MIX'
model_path = ''
assert len(model_path) > 0, 'Set the model path on the command line.'
checkpoint_name = 'ckpt_best_loss.pth'
experiment_dir = str(
get_new_folder(
path_template,
mkdir=False,
consider_mpi=True,
))
batch_size = 1
datasets = ["mix_2_spk_min_cv", "mix_2_spk_min_tt"]
locals() # Fix highlighting
def config():
debug = False
batch_size = 6
database_json = "" # Path to WSJ0_2mix .json
if "WSJ0_2MIX" in os.environ:
database_json = os.environ.get("WSJ0_2MIX")
assert len(database_json) > 0, 'Set path to database Json on the command line or set environment variable WSJ0_2MIX'
train_dataset = "mix_2_spk_min_tr"
validate_dataset = "mix_2_spk_min_cv"
# dict describing the model parameters, to allow changing the paramters from the command line.
# Configurable automatically inserts default values of not mentioned parameters to the config.json
trainer = {
"model": {
"factory": pt.contrib.examples.pit.model.PermutationInvariantTrainingModel,
"dropout_input": 0.,
"dropout_hidden": 0.,
"dropout_linear": 0.
},
"storage_dir": None,
"optimizer": {
"factory": pt.optimizer.Adam,
"gradient_clipping": 1
},
"summary_trigger": (1000, "iteration"),
"stop_trigger": (300_000, "iteration"),
"loss_weights": {
"pit_ips_loss": 1.0,
"pit_mse_loss": 0.0,
}
}
pt.Trainer.get_config(trainer)
if trainer['storage_dir'] is None:
trainer['storage_dir'] = get_new_folder(path_template, mkdir=False)
ex.observers.append(FileStorageObserver.create(
Path(trainer['storage_dir']) / 'sacred')
)
def get_storage_dir():
# Sacred should not add path_template to the config
# -> move this few lines to a function
path_template = Path(os.environ["STORAGE"]) / 'pth_models' / nickname
path_template.mkdir(exist_ok=True, parents=True)
return get_new_folder(path_template, mkdir=False)