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Scripts for proper BLEU evaluation, batch translation and averaging (#488)
`t2t-bleu` computes the "real" BLEU (giving the same result as [sacréBLEU](https://github.com/awslabs/sockeye/tree/master/contrib/sacrebleu) with `--tokenization intl` and as [mteval-v14.pl](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/mteval-v14.pl) with `--international-tokenization`). It can be used in two ways: * To evaluate an already translated file: `t2t-bleu --translation=my-wmt13.de --reference=wmt13_deen.de` * To evaluate all translations in a given directory. `t2t-translate-all` translates all checkpoints in a given directory. A custom command (e.g. SGE cluster wrapper) can be used instead of `t2t-decoder` for the translation. `t2t-avg-all` for each checkpoint in a given directory it averages it with the N preceding ones. All three scripts wait a given number of minutes for new checkpoints (produced by t2t-decoder, which can be run concurrently with these scripts).
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‎tensor2tensor/bin/t2t-avg-all

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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2017 The Tensor2Tensor Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Script to continously average last N checkpoints in a given directory."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import logging
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# Dependency imports
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import numpy as np
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import six
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from six.moves import zip # pylint: disable=redefined-builtin
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from collections import deque
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import shutil
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import tensorflow as tf
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from tensor2tensor.utils import bleu_hook
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flags = tf.flags
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FLAGS = flags.FLAGS
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flags.DEFINE_string("model_dir", "", "Directory to load model checkpoints from.")
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flags.DEFINE_string("output_dir", "avg/", "Directory to output the averaged checkpoints to.")
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flags.DEFINE_integer("n", 8, "How many checkpoints should be averaged?")
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flags.DEFINE_integer("min_steps", 0, "Ignore checkpoints with less steps.")
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flags.DEFINE_integer("wait_minutes", 0, "Wait upto N minutes for a new checkpoint.")
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def main(_):
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tf.logging._handler.setFormatter(logging.Formatter("%(asctime)s:" + logging.BASIC_FORMAT, None))
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tf.logging.set_verbosity(tf.logging.INFO)
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model_dir = os.path.expanduser(FLAGS.model_dir)
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output_dir = os.path.expanduser(FLAGS.output_dir)
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out_base_file = os.path.join(output_dir, 'model.ckpt')
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# Copy flags.txt with the original time, so t2t-bleu can report correct relative time.
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os.makedirs(FLAGS.output_dir, exist_ok=True)
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if not os.path.exists(os.path.join(output_dir, 'flags.txt')):
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shutil.copy2(os.path.join(model_dir, 'flags.txt'), os.path.join(output_dir, 'flags.txt'))
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models_processed = 0
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queue = deque()
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for model in bleu_hook.stepfiles_iterator(model_dir, FLAGS.wait_minutes, FLAGS.min_steps):
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if models_processed == 0:
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var_list = tf.contrib.framework.list_variables(model.filename)
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avg_values = {}
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for (name, shape) in var_list:
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if not name.startswith("global_step"):
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avg_values[name] = np.zeros(shape)
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models_processed += 1
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tf.logging.info("Loading [%d]: %s" % (models_processed, model.filename))
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reader = tf.contrib.framework.load_checkpoint(model.filename)
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for name in avg_values:
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avg_values[name] += reader.get_tensor(name) / FLAGS.n
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queue.append(model)
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if len(queue) < FLAGS.n:
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continue
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out_file = "%s-%d" % (out_base_file, model.steps)
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tf_vars = []
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tf.logging.info("Averaging %s" % (out_file))
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for (name, value) in six.iteritems(avg_values):
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tf_vars.append(tf.get_variable(name, shape=value.shape)) # TODO , dtype=var_dtypes[name]
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placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
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assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
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global_step = tf.Variable(model.steps, name="global_step", trainable=False, dtype=tf.int64)
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saver = tf.train.Saver(tf.global_variables())
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tf.logging.info("Running session for %s" % (out_file))
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for p, assign_op, (name, value) in zip(placeholders, assign_ops, six.iteritems(avg_values)):
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sess.run(assign_op, {p: value})
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tf.logging.info("Storing to %s" % out_file)
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saver.save(sess, out_base_file, global_step=global_step)
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os.utime(out_file + '.index', (model.mtime, model.mtime))
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tf.reset_default_graph()
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first_model = queue.popleft()
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reader = tf.contrib.framework.load_checkpoint(first_model.filename)
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for name in avg_values:
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avg_values[name] -= reader.get_tensor(name) / FLAGS.n
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if __name__ == "__main__":
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tf.app.run()

‎tensor2tensor/bin/t2t-bleu

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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2017 The Tensor2Tensor Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Evaluate BLEU score for all checkpoints/translations in a given directory.
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This script can be used in two ways.
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To evaluate one already translated file:
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`t2t-bleu --translation=my-wmt13.de --reference=wmt13_deen.de`
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To evaluate all translations in a given directory (translated by t2t-translate-all):
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`t2t-bleu
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--translations_dir=my-translations
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--reference=wmt13_deen.de
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--event_dir=events`
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In addition to the above-mentioned compulsory parameters,
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there are optional parameters:
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* bleu_variant: cased (case-sensitive), uncased, both (default).
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* tag_suffix: Default="", so the tags will be BLEU_cased and BLEU_uncased. tag_suffix
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can be used e.g. for different beam sizes if these should be plotted in different graphs.
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* min_steps: Don't evaluate checkpoints with less steps.
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Default=-1 means check the `last_evaluated_step.txt` file, which contains the number of steps
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of the last successfully evaluated checkpoint.
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* report_zero: Store BLEU=0 and guess its time based on the oldest file in the translations_dir.
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Default=True. This is useful, so TensorBoard reports correct relative time for the remaining
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checkpoints. This flag is set to False if min_steps is > 0.
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* wait_minutes: Wait upto N minutes for a new translated file. Default=0.
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This is useful for continuous evaluation of a running training,
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in which case this should be equal to save_checkpoints_secs/60 plus time needed for translation
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plus some reserve.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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from tensor2tensor.utils import bleu_hook
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import tensorflow as tf
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flags = tf.flags
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FLAGS = flags.FLAGS
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flags.DEFINE_string("source", None, "Path to the source-language file to be translated")
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flags.DEFINE_string("reference", None, "Path to the reference translation file")
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flags.DEFINE_string("translation", None, "Path to the MT system translation file")
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flags.DEFINE_string("translations_dir", None, "Directory with translated files to be evaulated.")
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flags.DEFINE_string("event_dir", None, "Where to store the event file.")
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flags.DEFINE_string("bleu_variant", "both",
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"Possible values: cased(case-sensitive), uncased, both(default).")
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flags.DEFINE_string("tag_suffix", "",
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"What to add to BLEU_cased and BLEU_uncased tags. Default=''.")
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flags.DEFINE_integer("min_steps", -1, "Don't evaluate checkpoints with less steps.")
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flags.DEFINE_integer("wait_minutes", 0,
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"Wait upto N minutes for a new checkpoint, cf. save_checkpoints_secs.")
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flags.DEFINE_bool("report_zero", None, "Store BLEU=0 and guess its time based on the oldest file.")
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def main(_):
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tf.logging.set_verbosity(tf.logging.INFO)
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if FLAGS.translation:
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if FLAGS.translations_dir:
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raise ValueError('Cannot specify both --translation and --translations_dir.')
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if FLAGS.bleu_variant in ('uncased', 'both'):
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bleu = 100 * bleu_hook.bleu_wrapper(FLAGS.reference, FLAGS.translation, case_sensitive=False)
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print("BLEU_uncased = %6.2f" % bleu)
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if FLAGS.bleu_variant in ('cased', 'both'):
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bleu = 100 * bleu_hook.bleu_wrapper(FLAGS.reference, FLAGS.translation, case_sensitive=True)
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print("BLEU_cased = %6.2f" % bleu)
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return
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if not FLAGS.translations_dir:
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raise ValueError('Either --translation or --translations_dir must be specified.')
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transl_dir = os.path.expanduser(FLAGS.translations_dir)
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last_step_file = os.path.join(FLAGS.event_dir, 'last_evaluated_step.txt')
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if FLAGS.min_steps == -1:
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try:
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with open(last_step_file) as ls_file:
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FLAGS.min_steps = int(ls_file.read())
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except FileNotFoundError:
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FLAGS.min_steps = 0
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if FLAGS.report_zero is None:
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FLAGS.report_zero = FLAGS.min_steps == 0
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writer = tf.summary.FileWriter(FLAGS.event_dir)
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for transl_file in bleu_hook.stepfiles_iterator(transl_dir, FLAGS.wait_minutes,
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FLAGS.min_steps, path_suffix=''):
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# report_zero handling must be inside the for-loop,
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# so we are sure the transl_dir is already created.
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if FLAGS.report_zero:
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all_files = (os.path.join(transl_dir, f) for f in os.listdir(transl_dir))
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start_time = min(os.path.getmtime(f) for f in all_files if os.path.isfile(f))
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values = []
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if FLAGS.bleu_variant in ('uncased', 'both'):
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values.append(tf.Summary.Value(tag='BLEU_uncased' + FLAGS.tag_suffix, simple_value=0))
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if FLAGS.bleu_variant in ('cased', 'both'):
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values.append(tf.Summary.Value(tag='BLEU_cased' + FLAGS.tag_suffix, simple_value=0))
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writer.add_event(tf.summary.Event(summary=tf.Summary(value=values),
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wall_time=start_time, step=0))
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FLAGS.report_zero = False
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filename = transl_file.filename
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tf.logging.info("Evaluating " + filename)
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values = []
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if FLAGS.bleu_variant in ('uncased', 'both'):
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bleu = 100 * bleu_hook.bleu_wrapper(FLAGS.reference, filename, case_sensitive=False)
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values.append(tf.Summary.Value(tag='BLEU_uncased' + FLAGS.tag_suffix, simple_value=bleu))
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tf.logging.info("%s: BLEU_uncased = %6.2f" % (filename, bleu))
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if FLAGS.bleu_variant in ('cased', 'both'):
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bleu = 100 * bleu_hook.bleu_wrapper(FLAGS.reference, filename, case_sensitive=True)
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values.append(tf.Summary.Value(tag='BLEU_cased' + FLAGS.tag_suffix, simple_value=bleu))
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tf.logging.info("%s: BLEU_cased = %6.2f" % (transl_file.filename, bleu))
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writer.add_event(tf.summary.Event(summary=tf.Summary(value=values),
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wall_time=transl_file.mtime, step=transl_file.steps))
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writer.flush()
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with open(last_step_file, 'w') as ls_file:
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ls_file.write(str(transl_file.steps) + '\n')
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if __name__ == "__main__":
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tf.app.run()

‎tensor2tensor/bin/t2t-translate-all

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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2017 The Tensor2Tensor Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Translate a file with all checkpoints in a given directory.
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t2t-decoder will be executed with these parameters:
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--problems
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--data_dir
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--output_dir with the value of --model_dir
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--decode_from_file with the value of --source
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--decode_hparams with properly formated --beam_size and --alpha
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--checkpoint_path automatically filled
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--decode_to_file automatically filled
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import shutil
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import tensorflow as tf
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from tensor2tensor.utils import bleu_hook
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flags = tf.flags
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# t2t-translate-all specific options
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flags.DEFINE_string("decoder_command", "t2t-decoder {params}",
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"Which command to execute instead t2t-decoder."
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"{params} is replaced by the parameters. Useful e.g. for qsub wrapper.")
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flags.DEFINE_string("model_dir", "", "Directory to load model checkpoints from.")
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flags.DEFINE_string("source", None, "Path to the source-language file to be translated")
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flags.DEFINE_string("translations_dir", "translations", "Where to store the translated files.")
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flags.DEFINE_integer("min_steps", 0, "Ignore checkpoints with less steps.")
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flags.DEFINE_integer("wait_minutes", 0, "Wait upto N minutes for a new checkpoint")
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# options derived from t2t-decoder
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flags.DEFINE_integer("beam_size", 4, "Beam-search width.")
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flags.DEFINE_float("alpha", 0.6, "Beam-search alpha.")
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flags.DEFINE_string("model", "transformer", "see t2t-decoder")
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flags.DEFINE_string("t2t_usr_dir", None, "see t2t-decoder")
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flags.DEFINE_string("data_dir", None, "see t2t-decoder")
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flags.DEFINE_string("problems", None, "see t2t-decoder")
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flags.DEFINE_string("hparams_set", "transformer_big_single_gpu", "see t2t-decoder")
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def main(_):
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FLAGS = flags.FLAGS
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tf.logging.set_verbosity(tf.logging.INFO)
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model_dir = os.path.expanduser(FLAGS.model_dir)
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translations_dir = os.path.expanduser(FLAGS.translations_dir)
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source = os.path.expanduser(FLAGS.source)
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os.makedirs(translations_dir, exist_ok=True)
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translated_base_file = os.path.join(translations_dir, FLAGS.problems)
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# Copy flags.txt with the original time, so t2t-bleu can report correct relative time.
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flags_path = os.path.join(translations_dir, FLAGS.problems + '-flags.txt')
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if not os.path.exists(flags_path):
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shutil.copy2(os.path.join(model_dir, 'flags.txt'), flags_path)
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for model in bleu_hook.stepfiles_iterator(model_dir, FLAGS.wait_minutes, FLAGS.min_steps):
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tf.logging.info("Translating " + model.filename)
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out_file = translated_base_file + '-' + str(model.steps)
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if os.path.exists(out_file):
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tf.logging.info(out_file + " already exists, so skipping it.")
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else:
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tf.logging.info("Translating " + out_file)
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params = ("--t2t_usr_dir={FLAGS.t2t_usr_dir} --output_dir={model_dir} "
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"--data_dir={FLAGS.data_dir} --problems={FLAGS.problems} "
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"--decode_hparams=beam_size={FLAGS.beam_size},alpha={FLAGS.alpha} "
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"--model={FLAGS.model} --hparams_set={FLAGS.hparams_set} "
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"--checkpoint_path={model.filename} --decode_from_file={source} "
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"--decode_to_file={out_file}".format(**locals()))
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command = FLAGS.decoder_command.format(**locals())
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tf.logging.info("Running:\n" + command)
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os.system(command)
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if __name__ == "__main__":
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tf.app.run()

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