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# Copyright 2017-2018 Intel Corporation
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# Changes Made from original:
# import paths
# ******************************************************************************
# Copyright 2017 Google Inc. All Rights Reserved.
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# you may not use this file except in compliance with the License.
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# ==============================================================================
"""Utility functions specifically for NMT."""
from __future__ import print_function
import codecs
import time
import numpy as np
import tensorflow as tf
from nlp_architect.models.gnmt.utils import evaluation_utils
from nlp_architect.models.gnmt.utils import misc_utils as utils
__all__ = ["decode_and_evaluate", "get_translation"]
[docs]def decode_and_evaluate(name,
model,
sess,
trans_file,
ref_file,
metrics,
subword_option,
beam_width,
tgt_eos,
num_translations_per_input=1,
decode=True,
infer_mode="greedy"):
"""Decode a test set and compute a score according to the evaluation task."""
# Decode
if decode:
utils.print_out(" decoding to output %s" % trans_file)
start_time = time.time()
num_sentences = 0
with codecs.getwriter("utf-8")(
tf.gfile.GFile(trans_file, mode="wb")) as trans_f:
trans_f.write("") # Write empty string to ensure file is created.
if infer_mode == "greedy":
num_translations_per_input = 1
elif infer_mode == "beam_search":
num_translations_per_input = min(num_translations_per_input, beam_width)
while True:
try:
nmt_outputs, _ = model.decode(sess)
if infer_mode != "beam_search":
nmt_outputs = np.expand_dims(nmt_outputs, 0)
batch_size = nmt_outputs.shape[1]
num_sentences += batch_size
for sent_id in range(batch_size):
for beam_id in range(num_translations_per_input):
translation = get_translation(
nmt_outputs[beam_id],
sent_id,
tgt_eos=tgt_eos,
subword_option=subword_option)
trans_f.write((translation + b"\n").decode("utf-8"))
except tf.errors.OutOfRangeError:
utils.print_time(
" done, num sentences %d, num translations per input %d" %
(num_sentences, num_translations_per_input), start_time)
break
# Evaluation
evaluation_scores = {}
if ref_file and tf.gfile.Exists(trans_file):
for metric in metrics:
score = evaluation_utils.evaluate(
ref_file,
trans_file,
metric,
subword_option=subword_option)
evaluation_scores[metric] = score
utils.print_out(" %s %s: %.1f" % (metric, name, score))
return evaluation_scores
[docs]def get_translation(nmt_outputs, sent_id, tgt_eos, subword_option):
"""Given batch decoding outputs, select a sentence and turn to text."""
if tgt_eos:
tgt_eos = tgt_eos.encode("utf-8")
# Select a sentence
output = nmt_outputs[sent_id, :].tolist()
# If there is an eos symbol in outputs, cut them at that point.
if tgt_eos and tgt_eos in output:
output = output[:output.index(tgt_eos)]
if subword_option == "bpe": # BPE
translation = utils.format_bpe_text(output)
elif subword_option == "spm": # SPM
translation = utils.format_spm_text(output)
else:
translation = utils.format_text(output)
return translation