Spaces:
Running
Running
File size: 3,734 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utility methods for accessing and operating on test data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import flags
import tensorflow as tf
from google.protobuf import text_format
import input as seq2species_input
from protos import seq2label_pb2
FLAGS = flags.FLAGS
# Target names included in the example inputs.
TEST_TARGETS = ['test_target_1', 'test_target_2']
def _as_bytes_feature(in_string):
"""Converts the given string to a tf.train.BytesList feature.
Args:
in_string: string to be converted to BytesList Feature.
Returns:
The TF BytesList Feature representing the given string.
"""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[in_string]))
def create_tmp_train_file(num_examples,
read_len,
characters=seq2species_input.DNA_BASES,
name='test.tfrecord'):
"""Write a test TFRecord of input examples to temporary test directory.
The generated input examples are test tf.train.Example protos, each comprised
of a toy sequence of length read_len and non-meaningful labels for targets in
TEST_TARGETS.
Args:
num_examples: int; number of examples to write to test input file.
read_len: int; length of test read sequences.
characters: string; set of characters from which to construct test reads.
Defaults to canonical DNA bases.
name: string; filename for the test input file.
Returns:
Full path to the generated temporary test input file.
"""
tmp_path = os.path.join(FLAGS.test_tmpdir, name)
with tf.python_io.TFRecordWriter(tmp_path) as writer:
for i in xrange(num_examples):
char = characters[i % len(characters)]
features_dict = {'sequence': _as_bytes_feature(char * read_len)}
for target_name in TEST_TARGETS:
nonsense_label = _as_bytes_feature(str(i))
features_dict[target_name] = nonsense_label
tf_features = tf.train.Features(feature=features_dict)
example = tf.train.Example(features=tf_features)
writer.write(example.SerializeToString())
return tmp_path
def create_tmp_metadata(num_examples, read_len):
"""Write a test Seq2LabelDatasetInfo test proto to temporary test directory.
Args:
num_examples: int; number of example labels to write into test metadata.
read_len: int; length of test read sequences.
Returns:
Full path to the generated temporary test file containing the
Seq2LabelDatasetInfo text proto.
"""
dataset_info = seq2label_pb2.Seq2LabelDatasetInfo(
read_length=read_len,
num_examples=num_examples,
read_stride=1,
dataset_path='test.tfrecord')
for target in TEST_TARGETS:
dataset_info.labels.add(
name=target, values=[str(i) for i in xrange(num_examples)])
tmp_path = os.path.join(FLAGS.test_tmpdir, 'test.pbtxt')
with tf.gfile.GFile(tmp_path, 'w') as f:
f.write(text_format.MessageToString(dataset_info))
return tmp_path
|