def __init__(self, dataset: str, repr: str, model: str, backwards: bool,
classifier_type: str):
fs = FS.for_classifier(dataset, repr, model, PretrainingType.FULL,
classifier_type)
text_field = fs.load_text_field()
super().__init__(repr=repr,
fs=fs,
text_field=text_field,
config_class=ClassifierTrainingConfig,
output_field=LEVEL_LABEL,
n_predictions=6 if classifier_type == 'level' else 2,
backwards=backwards)
def run_on_device(config: ClassifierConfig, force_rerun: bool) -> None:
base_model = config.base_model
pretraining = config.pretraining_type
PrepConfig.assert_classification_config(config.data.repr)
if bool(base_model) != bool(pretraining):
raise ValueError(
'Base model and pretraining_type params must be both set or both unset!'
)
fs = FS.for_classifier(config.data.dataset,
config.data.repr,
base_model=base_model,
pretraining=pretraining,
classification_type=config.classification_type)
fs.create_path_to_model(config.data, config.training_config)
attach_dataset_aware_handlers_to_loggers(fs.path_to_model, 'main.log')
print_gpu_info()
text_field = fs.load_text_field()
rnn_learner = create_nn_architecture(fs, text_field, LEVEL_LABEL,
config.data, config.arch,
config.min_log_coverage_percent)
logger.info(rnn_learner)
same_model_exists = fs.best_model_exists(rnn_learner)
if same_model_exists and not force_rerun:
logger.info(
f'Model {fs.path_to_classification_model} already trained. Not rerunning training.'
f'To retrain the model with this parameters, specify --force-rerun flag'
)
return
elif same_model_exists:
logger.info(
f"Model {fs.path_to_classification_model} already trained. Forcing rerun."
)
if pretraining == PretrainingType.FULL:
try:
logger.info(f'Trying to load base classifier: {base_model}')
fs.load_base_model(rnn_learner)
logger.info('Base classifier model is loaded.')
except Exception as e:
logger.warning(e)
logger.warning(
'Base classifier model not loaded. Training from scratch')
elif pretraining == PretrainingType.ONLY_ENCODER:
try:
logger.info(f'Trying to load pretarined LM: {base_model}')
# TODO its a dirty hack. fix it
fs.lm_cl_pretraining = True
fs.load_pretrained_langmodel(rnn_learner)
logger.info("Using pretrained LM")
except Exception as e:
logger.warning(e)
logger.warning('Pretrained LM not loaded. Training from scratch')
else:
logger.info("No pretraining. Training classifier from scratch.")
config_manager.save_config(config.training_config, fs.path_to_model)
train(fs, rnn_learner, config.training, config.metrics)
model = rnn_learner.model
to_test_mode(model)
sample_test_runs_file = os.path.join(fs.path_to_model, 'test_runs.out')
n_predicitions = 6 if config.classification_type == 'level' else 2
show_tests(fs.test_path, model, text_field, sample_test_runs_file,
config.data.backwards, n_predicitions, config.testing.n_samples)
logger.info("Classifier training finished successfully.")