def test_computation_target(temp_output_dir, train_opts, target_opts, atol): check_computation( Estimator, temp_output_dir, train_opts, target_opts, output_name=constants.TARGET_TAGS, expected_avg_probs=0.426000, atol=atol, ) # Testing resuming training resume_opts = argparse.Namespace(**vars(train_opts)) resume_opts.save_model = False resume_opts.checkpoint_save = True resume_opts.resume = True resume_opts.epochs += 1 check_computation( Estimator, temp_output_dir, resume_opts, target_opts, output_name=constants.TARGET_TAGS, expected_avg_probs=0.426000, atol=atol, )
def test_computation_target( tmp_path, xlm_model, xlm_tokenizer, xlm_model_dir, xlm_config_dict, train_config, data_config, big_atol, ): train_config['run']['output_dir'] = tmp_path train_config['data'] = data_config train_config['system'] = xlm_config_dict xlm_model.save_pretrained(tmp_path) xlm_tokenizer.save_pretrained(tmp_path) train_config['system']['model']['encoder']['model_name'] = str(tmp_path) check_computation( train_config, tmp_path, output_name=const.TARGET_TAGS, expected_avg_probs=0.410072, atol=big_atol, )
def test_computation_target( tmp_path, xlmr_model, xlmr_model_dir, xlmr_config_dict, train_config, data_config, big_atol, ): train_config['run']['output_dir'] = tmp_path train_config['data'] = data_config train_config['system'] = xlmr_config_dict xlmr_model.save_pretrained(tmp_path) train_config['system']['model']['encoder']['model_name'] = str(tmp_path) # When using `adamw` optimizer and the `optimizer.training_steps` are not set: with pytest.raises(ValueError): check_computation( train_config, tmp_path, output_name=const.TARGET_TAGS, expected_avg_probs=0.383413, atol=big_atol, ) # Now training will run: train_config['system']['optimizer']['training_steps'] = 10 check_computation( train_config, tmp_path, output_name=const.TARGET_TAGS, expected_avg_probs=0.383413, atol=big_atol, )
def test_computation_source(temp_output_dir, train_opts, source_opts, atol): check_computation( Estimator, temp_output_dir, train_opts, source_opts, output_name=constants.SOURCE_TAGS, expected_avg_probs=0.456631, atol=atol, )
def test_computation_gaps(temp_output_dir, train_opts, gap_opts, atol): check_computation( NuQE, temp_output_dir, train_opts, gap_opts, output_name=constants.GAP_TAGS, expected_avg_probs=0.454558, atol=atol, )
def test_computation_target(temp_output_dir, train_opts, target_opts, atol): check_computation( NuQE, temp_output_dir, train_opts, target_opts, output_name=constants.TARGET_TAGS, expected_avg_probs=0.466939, atol=atol, )
def test_computation(temp_output_dir, train_opts, nuqe_opts, atol): check_computation( NuQE, temp_output_dir, train_opts, nuqe_opts, output_name=constants.TARGET_TAGS, expected_avg_probs=0.572441, atol=atol, )
def test_computation(temp_output_dir, train_opts, quetch_opts, atol): check_computation( QUETCH, temp_output_dir, train_opts, quetch_opts, output_name=constants.TARGET_TAGS, expected_avg_probs=0.439731, atol=atol, )
def test_computation_source(temp_output_dir, train_opts, source_opts, atol): check_computation( QUETCH, temp_output_dir, train_opts, source_opts, output_name=constants.SOURCE_TAGS, expected_avg_probs=0.355306, atol=atol, )
def test_computation_gaps(temp_output_dir, train_opts, gap_opts, atol): check_computation( QUETCH, temp_output_dir, train_opts, gap_opts, output_name=constants.GAP_TAGS, expected_avg_probs=0.251563, atol=atol, )
def test_computation_targetgaps(tmp_path, output_targetgaps_config, train_config, data_config, big_atol): train_config['data'] = data_config train_config['system'] = output_targetgaps_config check_computation( train_config, tmp_path, output_name=const.TARGET_TAGS, expected_avg_probs=0.507699, atol=big_atol, )
def test_computation_source(tmp_path, output_source_config, train_config, data_config, big_atol): train_config['data'] = data_config train_config['system'] = output_source_config check_computation( train_config, tmp_path, output_name=const.SOURCE_TAGS, expected_avg_probs=0.486522, atol=big_atol, )
def test_computation_gaps(tmp_path, output_gaps_config, train_config, data_config, atol): train_config['data'] = data_config train_config['system'] = output_gaps_config check_computation( train_config, tmp_path, output_name=const.GAP_TAGS, expected_avg_probs=0.316064, atol=atol, )
def test_computation_target(tmp_path, output_target_config, train_config, data_config, atol): train_config['data'] = data_config train_config['system'] = output_target_config check_computation( train_config, tmp_path, output_name=const.TARGET_TAGS, expected_avg_probs=0.498354, atol=atol, )
def test_computation_gaps(temp_output_dir, train_opts, gap_opts, atol): gap_opts.predict_target = False check_computation( Estimator, temp_output_dir, train_opts, gap_opts, output_name=constants.GAP_TAGS, expected_avg_probs=0.322811, atol=atol, ) gap_opts.predict_target = True check_computation( Estimator, temp_output_dir, train_opts, gap_opts, output_name=constants.GAP_TAGS, expected_avg_probs=0.328905, atol=atol, )
def test_computation_target( tmp_path, bert_model, bert_model_dir, bert_config_dict, train_config, data_config, big_atol, ): train_config['run']['output_dir'] = tmp_path train_config['data'] = data_config train_config['system'] = bert_config_dict shutil.copy2(bert_model_dir / VOCAB_FILES_NAMES['vocab_file'], tmp_path) bert_model.save_pretrained(tmp_path) train_config['system']['model']['encoder']['model_name'] = str(tmp_path) check_computation( train_config, tmp_path, output_name=const.TARGET_TAGS, expected_avg_probs=0.550805, atol=big_atol, )