def decode_sequence(features, areas, hparams, decode_length,
post_processing=True):
"""Decodes the entire sequence in an auto-regressive way."""
decode_utils.decode_n_step(seq2act_model.compute_logits,
features, areas,
hparams, n=decode_length, beam_size=1)
if post_processing:
features["input_refs"] = decode_utils.unify_input_ref(
features["verbs"], features["input_refs"])
pred_lengths = decode_utils.verb_refs_to_lengths(features["task"],
features["verb_refs"],
include_eos=False)
predicted_actions = tf.concat([
features["verb_refs"],
features["obj_refs"],
features["input_refs"],
tf.to_int32(tf.expand_dims(features["verbs"], 2)),
tf.to_int32(tf.expand_dims(features["objects"], 2))], axis=-1)
if post_processing:
predicted_actions = tf.where(
tf.tile(tf.expand_dims(
tf.sequence_mask(pred_lengths,
maxlen=tf.shape(predicted_actions)[1]),
2), [1, 1, tf.shape(predicted_actions)[-1]]), predicted_actions,
tf.zeros_like(predicted_actions))
return predicted_actions
def _ref_accuracy(features,
pred_dict,
nonpadding,
name,
metrics,
decode_refs=None,
measure_beginning_eos=False,
debug=False):
"""Computes the accuracy of reference prediction.
Args:
features: the feature dict.
pred_dict: the dictionary to hold the prediction results.
nonpadding: a 2D boolean tensor for masking out paddings.
name: the name of the feature to be predicted.
metrics: the eval metrics.
decode_refs: decoded references.
measure_beginning_eos: whether to measure the beginning and the end.
debug: whether to output mismatches.
"""
if decode_refs is not None:
gt_seq_lengths = decode_utils.verb_refs_to_lengths(
features["task"], features["verb_refs"])
pr_seq_lengths = decode_utils.verb_refs_to_lengths(
decode_refs["task"], decode_refs["verb_refs"])
full_acc, partial_acc = decode_utils.sequence_accuracy(
features[name],
decode_refs[name],
gt_seq_lengths,
pr_seq_lengths,
debug=debug,
name=name)
metrics[name + "_full_accuracy"] = tf.metrics.mean(full_acc)
metrics[name + "_partial_accuracy"] = tf.metrics.mean(partial_acc)
if measure_beginning_eos:
nonpadding = tf.reshape(nonpadding, [-1])
refs = tf.reshape(features[name], [-1, 2])
predict_refs = tf.reshape(pred_dict[name], [-1, 2])
metrics[name + "_start"] = tf.metrics.accuracy(
labels=tf.boolean_mask(refs[:, 0], nonpadding),
predictions=tf.boolean_mask(predict_refs[:, 0], nonpadding),
name=name + "_start_accuracy")
metrics[name + "_end"] = tf.metrics.accuracy(
labels=tf.boolean_mask(refs[:, 1], nonpadding),
predictions=tf.boolean_mask(predict_refs[:, 1], nonpadding),
name=name + "_end_accuracy")