def retrieve_untrainable_method(method_def):
lm_pre_test, lm_post_test = None, None
if isinstance(method_def, str):
name = method_def
else:
name = method_def['name']
lm_pre_test_def = method_def.get('lm_pre_test')
lm_post_test_def = method_def.get('lm_post_test')
if lm_pre_test_def is not None:
lm_pre_test = retrieve_lm_processes(lm_pre_test_def)
if lm_post_test_def is not None:
lm_post_test = retrieve_lm_processes(lm_post_test_def)
test, metadata = load_and_validate_untrainable_method_module(name)
return Test(test, name, metadata, lm_pre_test, lm_post_test)
def retrieve_dataset(dataset_def):
lm_process = None
if isinstance(dataset_def, str):
name = dataset_def
else:
name = dataset_def['name']
lm_process_def = dataset_def.get('lm_post_load')
if lm_process_def is not None:
# user is specifying some landmark processing
lm_process = retrieve_lm_processes(lm_process_def)
dataset_gen_f, metadata = load_and_validate_dataset_module(name)
return Dataset(dataset_gen_f, name, metadata, lm_post_load=lm_process)
def retrieve_dataset(dataset_def):
lm_process = None
if isinstance(dataset_def, str):
name = dataset_def
else:
name = dataset_def['name']
lm_process_def = dataset_def.get('lm_post_load')
if lm_process_def is not None:
# user is specifying some landmark processing
lm_process = retrieve_lm_processes(lm_process_def)
dataset_gen_f, metadata = load_and_validate_dataset_module(name)
return Dataset(dataset_gen_f, name, metadata, lm_post_load=lm_process)