def after_ep_func(epoch): out_fp = comb_paths(run_hp.output_path, 'ep%d_%s' % (epoch, run_hp.checkpoint_full_fn)) imodel.save_state(out_fp) gen_folder_path = comb_paths(run_hp.output_path, "output_ep%d" % epoch) summ_eval(output_folder=gen_folder_path, data_pipeline=eval_data_pipeline, eval_data_source=eval_dev_data_source, summ_gen_func=partial(idev.summ_generator, summ_post_proc=summ_post_proc), rev_formatter_func=idev.format_revs, avg_rouge=True, sent_splitter=run_hp.sent_split_func, analytics_func=run_hp.analytics_func) gen_seqs(data_sources=gen_data_sources, output_folder=gen_folder_path, gen_func=partial(idev.gen_and_save_summs))
# AFTER TRAINING PROCEDURES # gen_folder_path = comb_paths(run_hp.output_path, "output") summ_eval(output_folder=gen_folder_path, eval_data_source=eval_test_data_source, summ_gen_func=partial(idev.summ_generator, summ_post_proc=summ_post_proc), data_pipeline=eval_data_pipeline, rev_formatter_func=idev.format_revs, avg_rouge=True, sent_splitter=run_hp.sent_split_func, analytics_func=run_hp.analytics_func) gen_seqs(data_sources=gen_data_sources, output_folder=gen_folder_path, gen_func=partial(idev.gen_and_save_summs)) else: # inference procedure where summaries are generated for reviews in CSV # files infer_bsz = parser_args.infer_batch_size infer_inp_file_path = parser_args.infer_input_file_path out_file_name = get_file_name(infer_inp_file_path) infer_out_file_path = comb_paths(run_hp.output_path, f'{out_file_name}.out.txt') assert infer_inp_file_path is not None rev_num = get_rev_number(infer_inp_file_path) logger.info("Performing inference/summary generation") infer_data_pipeline = assemble_infer_pipeline(