410992318552115d2e3de84844bf523012e3e98e,thumt/utils/hooks.py,,_evaluate,#,133

Before Change

    with graph.as_default():
        features = input_fn()
        refs = features["references"]
        placeholders = {
            "source": tf.placeholder(tf.int32, [None, None], "source"),
            "source_length": tf.placeholder(tf.int32, [None], "source_length")
        }
        predictions = eval_fn(placeholders)
        predictions = predictions[0][:, 0, :]

        all_refs = [[] for _ in range(len(refs))]
        all_outputs = []

        sess_creator = tf.train.ChiefSessionCreator(
            checkpoint_dir=path,
            config=config
        )

        with tf.train.MonitoredSession(session_creator=sess_creator) as sess:
            while not sess.should_stop():
                feats = sess.run(features)
                outputs = sess.run(predictions, feed_dict={
                    placeholders["source"]: feats["source"],
                    placeholders["source_length"]: feats["source_length"]
                })
                // shape: [batch, len]
                outputs = outputs.tolist()
                // shape: ([batch, len], ..., [batch, len])
                references = [item.tolist() for item in feats["references"]]

                all_outputs.extend(outputs)

After Change

    with graph.as_default():
        features = input_fn()
        refs = features["references"]
        placeholders = []
        for i in range(len(device_list)):
            placeholders.append({
                "source": tf.placeholder(tf.int32, [None, None],
                                         "source_%d" % i),
                "source_length": tf.placeholder(tf.int32, [None],
                                                "source_length_%d" % i)
            })
        predictions = parallel.data_parallelism(
            device_list, eval_fn, placeholders)
        predictions = [pred[0][:, 0, :] for pred in predictions]

        all_refs = [[] for _ in range(len(refs))]
        all_outputs = []

        sess_creator = tf.train.ChiefSessionCreator(
            checkpoint_dir=path,
            config=config
        )

        with tf.train.MonitoredSession(session_creator=sess_creator) as sess:
            while not sess.should_stop():
                feats = sess.run(features)
                inp_feats = {
                    "source": feats["source"],
                    "source_length": feats["source_length"]
                }
                op, feed_dict = _shard_features(inp_feats, placeholders,
                                                predictions)
                // A list of numpy array with shape: [batch, len]
                outputs = sess.run(op, feed_dict=feed_dict)

                for shard in outputs:

Instances