Пример #1
0
def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)
    tf.get_logger().propagate = False

    albert_config = modeling.AlbertConfig.from_json_file(
        FLAGS.albert_config_file)

    validate_flags_or_throw(albert_config)

    tf.gfile.MakeDirs(FLAGS.output_dir)
    print("Output:", FLAGS.output_dir)

    tokenizer = fine_tuning_utils.create_vocab(
        vocab_file=FLAGS.vocab_file,
        do_lower_case=FLAGS.do_lower_case,
        spm_model_file=FLAGS.spm_model_file,
        hub_module=FLAGS.albert_hub_module_handle)

    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

    is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
    if FLAGS.do_train:
        iterations_per_loop = int(
            min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
    else:
        iterations_per_loop = FLAGS.iterations_per_loop
    run_config = contrib_tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        keep_checkpoint_max=0,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=contrib_tpu.TPUConfig(
            iterations_per_loop=iterations_per_loop,
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    train_examples = squad_utils.read_squad_examples(
        input_file=FLAGS.train_file, is_training=True)
    num_train_steps = int(
        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    if FLAGS.do_train:
        num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

        # Pre-shuffle the input to avoid having to make a very large shuffle
        # buffer in in the `input_fn`.
        rng = random.Random(12345)
        rng.shuffle(train_examples)

    model_fn = squad_utils.v2_model_fn_builder(
        albert_config=albert_config,
        init_checkpoint=FLAGS.init_checkpoint,
        learning_rate=FLAGS.learning_rate,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_tpu,
        max_seq_length=FLAGS.max_seq_length,
        start_n_top=FLAGS.start_n_top,
        end_n_top=FLAGS.end_n_top,
        dropout_prob=FLAGS.dropout_prob,
        hub_module=FLAGS.albert_hub_module_handle)

    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = contrib_tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)

    if FLAGS.do_train:
        # We write to a temporary file to avoid storing very large constant tensors
        # in memory.

        if not tf.gfile.Exists(FLAGS.train_feature_file):
            train_writer = squad_utils.FeatureWriter(filename=os.path.join(
                FLAGS.train_feature_file),
                                                     is_training=True)
            squad_utils.convert_examples_to_features(
                examples=train_examples,
                tokenizer=tokenizer,
                max_seq_length=FLAGS.max_seq_length,
                doc_stride=FLAGS.doc_stride,
                max_query_length=FLAGS.max_query_length,
                is_training=True,
                output_fn=train_writer.process_feature,
                do_lower_case=FLAGS.do_lower_case)
            train_writer.close()

        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num orig examples = %d", len(train_examples))
        # tf.logging.info("  Num split examples = %d", train_writer.num_features)
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        del train_examples

        train_input_fn = squad_utils.input_fn_builder(
            input_file=FLAGS.train_feature_file,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True,
            use_tpu=FLAGS.use_tpu,
            bsz=FLAGS.train_batch_size,
            is_v2=True)
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

    if FLAGS.do_predict:
        with tf.gfile.Open(FLAGS.predict_file) as predict_file:
            prediction_json = json.load(predict_file)["data"]
        eval_examples = squad_utils.read_squad_examples(
            input_file=FLAGS.predict_file, is_training=False)

        if (tf.gfile.Exists(FLAGS.predict_feature_file)
                and tf.gfile.Exists(FLAGS.predict_feature_left_file)):
            tf.logging.info("Loading eval features from {}".format(
                FLAGS.predict_feature_left_file))
            with tf.gfile.Open(FLAGS.predict_feature_left_file, "rb") as fin:
                eval_features = pickle.load(fin)
        else:
            eval_writer = squad_utils.FeatureWriter(
                filename=FLAGS.predict_feature_file, is_training=False)
            eval_features = []

            def append_feature(feature):
                eval_features.append(feature)
                eval_writer.process_feature(feature)

            squad_utils.convert_examples_to_features(
                examples=eval_examples,
                tokenizer=tokenizer,
                max_seq_length=FLAGS.max_seq_length,
                doc_stride=FLAGS.doc_stride,
                max_query_length=FLAGS.max_query_length,
                is_training=False,
                output_fn=append_feature,
                do_lower_case=FLAGS.do_lower_case)
            eval_writer.close()

            with tf.gfile.Open(FLAGS.predict_feature_left_file, "wb") as fout:
                pickle.dump(eval_features, fout)

        tf.logging.info("***** Running predictions *****")
        tf.logging.info("  Num orig examples = %d", len(eval_examples))
        tf.logging.info("  Num split examples = %d", len(eval_features))
        tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)

        predict_input_fn = squad_utils.input_fn_builder(
            input_file=FLAGS.predict_feature_file,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=False,
            use_tpu=FLAGS.use_tpu,
            bsz=FLAGS.predict_batch_size,
            is_v2=True)

        def get_result(checkpoint):
            """Evaluate the checkpoint on SQuAD v2.0."""
            # If running eval on the TPU, you will need to specify the number of
            # steps.
            reader = tf.train.NewCheckpointReader(checkpoint)
            global_step = reader.get_tensor(tf.GraphKeys.GLOBAL_STEP)
            all_results = []
            for result in estimator.predict(predict_input_fn,
                                            yield_single_examples=True,
                                            checkpoint_path=checkpoint):
                if len(all_results) % 1000 == 0:
                    tf.logging.info("Processing example: %d" %
                                    (len(all_results)))
                unique_id = int(result["unique_ids"])
                start_top_log_probs = ([
                    float(x) for x in result["start_top_log_probs"].flat
                ])
                start_top_index = [
                    int(x) for x in result["start_top_index"].flat
                ]
                end_top_log_probs = ([
                    float(x) for x in result["end_top_log_probs"].flat
                ])
                end_top_index = [int(x) for x in result["end_top_index"].flat]

                cls_logits = float(result["cls_logits"].flat[0])
                all_results.append(
                    squad_utils.RawResultV2(
                        unique_id=unique_id,
                        start_top_log_probs=start_top_log_probs,
                        start_top_index=start_top_index,
                        end_top_log_probs=end_top_log_probs,
                        end_top_index=end_top_index,
                        cls_logits=cls_logits))

            output_prediction_file = os.path.join(FLAGS.output_dir,
                                                  "predictions.json")
            output_nbest_file = os.path.join(FLAGS.output_dir,
                                             "nbest_predictions.json")
            output_null_log_odds_file = os.path.join(FLAGS.output_dir,
                                                     "null_odds.json")

            result_dict = {}
            cls_dict = {}
            squad_utils.accumulate_predictions_v2(
                result_dict, cls_dict, eval_examples, eval_features,
                all_results, FLAGS.n_best_size, FLAGS.max_answer_length,
                FLAGS.start_n_top, FLAGS.end_n_top)

            return squad_utils.evaluate_v2(
                result_dict, cls_dict, prediction_json, eval_examples,
                eval_features, all_results, FLAGS.n_best_size,
                FLAGS.max_answer_length, output_prediction_file,
                output_nbest_file, output_null_log_odds_file), int(global_step)

        def _find_valid_cands(curr_step):
            filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
            candidates = []
            for filename in filenames:
                if filename.endswith(".index"):
                    ckpt_name = filename[:-6]
                    idx = ckpt_name.split("-")[-1]
                    if idx != "best" and int(idx) > curr_step:
                        candidates.append(filename)
            return candidates

        output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
        checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
        key_name = "f1"
        writer = tf.gfile.GFile(output_eval_file, "w")
        if tf.gfile.Exists(checkpoint_path + ".index"):
            result = get_result(checkpoint_path)
            best_perf = result[0][key_name]
            global_step = result[1]
        else:
            global_step = -1
            best_perf = -1
            checkpoint_path = None
        while global_step < num_train_steps:
            steps_and_files = {}
            filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
            for filename in filenames:
                if filename.endswith(".index"):
                    ckpt_name = filename[:-6]
                    cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
                    if cur_filename.split("-")[-1] == "best":
                        continue
                    gstep = int(cur_filename.split("-")[-1])
                    if gstep not in steps_and_files:
                        tf.logging.info(
                            "Add {} to eval list.".format(cur_filename))
                        steps_and_files[gstep] = cur_filename
            tf.logging.info("found {} files.".format(len(steps_and_files)))
            if not steps_and_files:
                tf.logging.info(
                    "found 0 file, global step: {}. Sleeping.".format(
                        global_step))
                time.sleep(60)
            else:
                for ele in sorted(steps_and_files.items()):
                    step, checkpoint_path = ele
                    print("GS: ", global_step, step)
                    if global_step >= step:
                        if len(_find_valid_cands(step)) > 1:
                            for ext in [
                                    "meta", "data-00000-of-00001", "index"
                            ]:
                                src_ckpt = checkpoint_path + ".{}".format(ext)
                                tf.logging.info("removing {}".format(src_ckpt))
                                tf.gfile.Remove(src_ckpt)
                        continue
                    result, global_step = get_result(checkpoint_path)
                    print("EVAL RESULTS")
                    tf.logging.info("***** Eval results *****")
                    for key in sorted(result.keys()):
                        tf.logging.info("  %s = %s", key, str(result[key]))
                        writer.write("%s = %s\n" % (key, str(result[key])))
                    if result[key_name] > best_perf:
                        best_perf = result[key_name]
                        for ext in ["meta", "data-00000-of-00001", "index"]:
                            src_ckpt = checkpoint_path + ".{}".format(ext)
                            tgt_ckpt = checkpoint_path.rsplit(
                                "-", 1)[0] + "-best.{}".format(ext)
                            tf.logging.info("saving {} to {}".format(
                                src_ckpt, tgt_ckpt))
                            tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
                            writer.write("saved {} to {}\n".format(
                                src_ckpt, tgt_ckpt))
                    writer.write("best {} = {}\n".format(key_name, best_perf))
                    tf.logging.info("  best {} = {}\n".format(
                        key_name, best_perf))

                    if len(_find_valid_cands(global_step)) > 2:
                        for ext in ["meta", "data-00000-of-00001", "index"]:
                            src_ckpt = checkpoint_path + ".{}".format(ext)
                            tf.logging.info("removing {}".format(src_ckpt))
                            tf.gfile.Remove(src_ckpt)
                    writer.write("=" * 50 + "\n")
            print("Sleeping")
            time.sleep(10)
        checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
        result, global_step = get_result(checkpoint_path)
        tf.logging.info("***** Final Eval results *****")
        for key in sorted(result.keys()):
            tf.logging.info("  %s = %s", key, str(result[key]))
            writer.write("%s = %s\n" % (key, str(result[key])))
        writer.write("best perf happened at step: {}".format(global_step))
Пример #2
0
def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)

    albert_config = modeling.AlbertConfig.from_json_file(
        FLAGS.albert_config_file)

    validate_flags_or_throw(albert_config)

    tf.gfile.MakeDirs(FLAGS.output_dir)

    tokenizer = fine_tuning_utils.create_vocab(
        vocab_file=FLAGS.vocab_file,
        do_lower_case=FLAGS.do_lower_case,
        spm_model_file=FLAGS.spm_model_file,
        hub_module=FLAGS.albert_hub_module_handle)

    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

    is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
    if FLAGS.do_train:
        iterations_per_loop = int(
            min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
    else:
        iterations_per_loop = FLAGS.iterations_per_loop
    run_config = contrib_tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        keep_checkpoint_max=0,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=contrib_tpu.TPUConfig(
            iterations_per_loop=iterations_per_loop,
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    # if FLAGS.do_train:
    #     train_examples = squad_utils.read_squad_examples(
    #         input_file=FLAGS.train_file, is_training=True)
    #     num_train_steps = int(
    #         len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    #     num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
    #
    #     # Pre-shuffle the input to avoid having to make a very large shuffle
    #     # buffer in in the `input_fn`.
    #     rng = random.Random(12345)
    #     rng.shuffle(train_examples)

    model_fn = squad_utils.v2_model_fn_builder(
        albert_config=albert_config,
        init_checkpoint=FLAGS.init_checkpoint,
        learning_rate=FLAGS.learning_rate,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_tpu,
        max_seq_length=FLAGS.max_seq_length,
        start_n_top=FLAGS.start_n_top,
        end_n_top=FLAGS.end_n_top,
        dropout_prob=FLAGS.dropout_prob,
        hub_module=FLAGS.albert_hub_module_handle)

    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = contrib_tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)

    if FLAGS.do_train:
        # We write to a temporary file to avoid storing very large constant tensors
        # in memory.

        if not tf.gfile.Exists(FLAGS.train_feature_file):
            train_writer = squad_utils.FeatureWriter(filename=os.path.join(
                FLAGS.train_feature_file),
                                                     is_training=True)
            squad_utils.convert_examples_to_features(
                examples=train_examples,
                tokenizer=tokenizer,
                max_seq_length=FLAGS.max_seq_length,
                doc_stride=FLAGS.doc_stride,
                max_query_length=FLAGS.max_query_length,
                is_training=True,
                output_fn=train_writer.process_feature,
                do_lower_case=FLAGS.do_lower_case)
            train_writer.close()

        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num orig examples = %d", len(train_examples))
        # tf.logging.info("  Num split examples = %d", train_writer.num_features)
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        del train_examples

        train_input_fn = squad_utils.input_fn_builder(
            input_file=FLAGS.train_feature_file,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True,
            use_tpu=FLAGS.use_tpu,
            bsz=FLAGS.train_batch_size,
            is_v2=True)
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

    if FLAGS.do_predict:
        with tf.gfile.Open(FLAGS.predict_file) as predict_file:
            prediction_json = json.load(predict_file)["data"]
        eval_examples = squad_utils.read_squad_examples(
            input_file=FLAGS.predict_file, is_training=False)

        if (tf.gfile.Exists(FLAGS.predict_feature_file)
                and tf.gfile.Exists(FLAGS.predict_feature_left_file)):
            tf.logging.info("Loading eval features from {}".format(
                FLAGS.predict_feature_left_file))
            with tf.gfile.Open(FLAGS.predict_feature_left_file, "rb") as fin:
                eval_features = pickle.load(fin)
        else:
            eval_writer = squad_utils.FeatureWriter(
                filename=FLAGS.predict_feature_file, is_training=False)
            eval_features = []

            def append_feature(feature):
                eval_features.append(feature)
                eval_writer.process_feature(feature)

            squad_utils.convert_examples_to_features(
                examples=eval_examples,
                tokenizer=tokenizer,
                max_seq_length=FLAGS.max_seq_length,
                doc_stride=FLAGS.doc_stride,
                max_query_length=FLAGS.max_query_length,
                is_training=False,
                output_fn=append_feature,
                do_lower_case=FLAGS.do_lower_case)
            eval_writer.close()

            with tf.gfile.Open(FLAGS.predict_feature_left_file, "wb") as fout:
                pickle.dump(eval_features, fout)

        tf.logging.info("***** Running predictions *****")
        tf.logging.info("  Num orig examples = %d", len(eval_examples))
        tf.logging.info("  Num split examples = %d", len(eval_features))
        tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)

        predict_input_fn = squad_utils.input_fn_builder(
            input_file=FLAGS.predict_feature_file,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=False,
            use_tpu=FLAGS.use_tpu,
            bsz=FLAGS.predict_batch_size,
            is_v2=True)

        def get_result(checkpoint):
            """Evaluate the checkpoint on SQuAD v2.0."""
            # If running eval on the TPU, you will need to specify the number of
            # steps.
            reader = tf.train.NewCheckpointReader(checkpoint)
            global_step = reader.get_tensor(tf.GraphKeys.GLOBAL_STEP)
            all_results = []
            for result in estimator.predict(predict_input_fn,
                                            yield_single_examples=True,
                                            checkpoint_path=checkpoint):
                if len(all_results) % 1000 == 0:
                    tf.logging.info("Processing example: %d" %
                                    (len(all_results)))
                unique_id = int(result["unique_ids"])

                cls_logits = float(result["cls_logits"].flat[0])
                all_results.append(
                    squad_utils.RawResultV2(unique_id=unique_id,
                                            cls_logits=cls_logits))

            output_prediction_file = os.path.join(FLAGS.output_dir,
                                                  "predictions.json")
            output_nbest_file = os.path.join(FLAGS.output_dir,
                                             "nbest_predictions.json")
            output_null_log_odds_file = os.path.join(FLAGS.output_dir,
                                                     "null_odds.json")

            result_dict = {}
            cls_dict = {}
            squad_utils.accumulate_predictions_v2(
                result_dict, cls_dict, eval_examples, eval_features,
                all_results, FLAGS.n_best_size, FLAGS.max_answer_length,
                FLAGS.start_n_top, FLAGS.end_n_top)

            from squad_utils import make_qid_to_has_ans
            import numpy as np
            qid_to_has_ans = make_qid_to_has_ans(
                prediction_json)  # maps qid to True/False
            has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
            no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
            print("has_ans", len(has_ans_qids))
            print("no_ans", len(no_ans_qids))

            def compute_metrics_with_threshold(threshold):
                nonlocal result_dict
                result_dict = {}
                tp = 0
                tn = 0
                fp = 0
                fn = 0
                for example_index, example in enumerate(eval_examples):
                    m = np.min(cls_dict[example_index])
                    predict_is_impossible = 1 / (1 + np.exp(-m)) > threshold
                    # predict_is_impossible = m > threshold
                    result_dict[example.qas_id] = m
                    if example.is_impossible:
                        if predict_is_impossible:
                            tp += 1
                        else:
                            fn += 1
                    else:
                        if predict_is_impossible:
                            fp += 1
                        else:
                            tn += 1
                precision = tp / (tp + fp)
                recall = tp / (fn + tp)
                f1 = 2 * tp / (2 * tp + fp + fn)
                tf.logging.info(f"precision: {precision}"
                                f"recall: {recall}"
                                f"f1: {f1}")
                return precision, recall, f1

            # precision, recall, f1 = compute_metrics_with_threshold(0.4)
            precision, recall, f1 = compute_metrics_with_threshold(0.5)
            # precision, recall, f1 = compute_metrics_with_threshold(0.6)

            with tf.gfile.GFile(output_prediction_file, "w") as writer:
                writer.write(json.dumps(result_dict, indent=4) + "\n")

            return {
                "precision": precision,
                "recall": recall,
                "f1": f1,
                "total": len(eval_examples)
            }, int(global_step)

        def _find_valid_cands(curr_step):
            filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
            candidates = []
            for filename in filenames:
                if filename.endswith(".index"):
                    ckpt_name = filename[:-6]
                    idx = ckpt_name.split("-")[-1]
                    if idx != "best" and int(idx) > curr_step:
                        candidates.append(filename)
            return candidates

        # output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
        # checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
        # key_name = "f1"
        # writer = tf.gfile.GFile(output_eval_file, "w")
        # if tf.gfile.Exists(checkpoint_path + ".index"):
        #     result = get_result(checkpoint_path)
        #     best_perf = result[0][key_name]
        #     global_step = result[1]
        # else:
        #     global_step = -1
        #     best_perf = -1
        #     checkpoint_path = None
        # while global_step < num_train_steps:
        #     steps_and_files = {}
        #     filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
        #     for filename in filenames:
        #         if filename.endswith(".index"):
        #             ckpt_name = filename[:-6]
        #             cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
        #             if cur_filename.split("-")[-1] == "best":
        #                 continue
        #             gstep = int(cur_filename.split("-")[-1])
        #             if gstep not in steps_and_files:
        #                 tf.logging.info("Add {} to eval list.".format(cur_filename))
        #                 steps_and_files[gstep] = cur_filename
        #     tf.logging.info("found {} files.".format(len(steps_and_files)))
        #     if not steps_and_files:
        #         tf.logging.info("found 0 file, global step: {}. Sleeping."
        #                         .format(global_step))
        #         time.sleep(60)
        #     else:
        #         for ele in sorted(steps_and_files.items()):
        #             step, checkpoint_path = ele
        #             if global_step >= step:
        #                 if len(_find_valid_cands(step)) > 1:
        #                     for ext in ["meta", "data-00000-of-00001", "index"]:
        #                         src_ckpt = checkpoint_path + ".{}".format(ext)
        #                         tf.logging.info("removing {}".format(src_ckpt))
        #                         tf.gfile.Remove(src_ckpt)
        #                 continue
        #             result, global_step = get_result(checkpoint_path)
        #             tf.logging.info("***** Eval results *****")
        #             for key in sorted(result.keys()):
        #                 tf.logging.info("  %s = %s", key, str(result[key]))
        #                 writer.write("%s = %s\n" % (key, str(result[key])))
        #             if result[key_name] > best_perf:
        #                 best_perf = result[key_name]
        #                 for ext in ["meta", "data-00000-of-00001", "index"]:
        #                     src_ckpt = checkpoint_path + ".{}".format(ext)
        #                     tgt_ckpt = checkpoint_path.rsplit(
        #                         "-", 1)[0] + "-best.{}".format(ext)
        #                     tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
        #                     tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
        #                     writer.write("saved {} to {}\n".format(src_ckpt, tgt_ckpt))
        #             writer.write("best {} = {}\n".format(key_name, best_perf))
        #             tf.logging.info("  best {} = {}\n".format(key_name, best_perf))
        #
        #             if len(_find_valid_cands(global_step)) > 2:
        #                 for ext in ["meta", "data-00000-of-00001", "index"]:
        #                     src_ckpt = checkpoint_path + ".{}".format(ext)
        #                     tf.logging.info("removing {}".format(src_ckpt))
        #                     tf.gfile.Remove(src_ckpt)
        #             writer.write("=" * 50 + "\n")

        result, global_step = get_result(FLAGS.init_checkpoint)