def _eval_all(sess): """Gathers all metrics for a ckpt.""" summaries = collections.defaultdict(list) if eval_gold: for midi_notes, buttons, seq_varlen in gold.gold_iterator([-6, 6]): gold_diff_l1_seq, gold_diff_l2_seq = sess.run( [gold_diff_l1, gold_diff_l2], { gold_feat_dict["midi_pitches"]: midi_notes, gold_feat_dict["delta_times_int"]: np.ones_like(midi_notes) * 8, gold_seq_varlens: [seq_varlen], gold_buttons: buttons }) summaries["gold_diff_l1"].append(gold_diff_l1_seq) summaries["gold_diff_l2"].append(gold_diff_l2_seq) while True: try: batches = sess.run(summary_name_to_batch_tensor) except tf.errors.OutOfRangeError: break for name, scalar in batches.items(): summaries[name].append(scalar) return summaries