287 lines
9.8 KiB
Python
287 lines
9.8 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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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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# ==============================================================================
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"""Tests for data_utils."""
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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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from itertools import cycle
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import os
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import tarfile
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import zipfile
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import numpy as np
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from six.moves.urllib.parse import urljoin
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from six.moves.urllib.request import pathname2url
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from tensorflow.python import keras
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.platform import test
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class TestGetFileAndValidateIt(test.TestCase):
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def test_get_file_and_validate_it(self):
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"""Tests get_file from a url, plus extraction and validation.
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"""
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dest_dir = self.get_temp_dir()
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orig_dir = self.get_temp_dir()
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text_file_path = os.path.join(orig_dir, 'test.txt')
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zip_file_path = os.path.join(orig_dir, 'test.zip')
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tar_file_path = os.path.join(orig_dir, 'test.tar.gz')
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with open(text_file_path, 'w') as text_file:
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text_file.write('Float like a butterfly, sting like a bee.')
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with tarfile.open(tar_file_path, 'w:gz') as tar_file:
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tar_file.add(text_file_path)
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with zipfile.ZipFile(zip_file_path, 'w') as zip_file:
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zip_file.write(text_file_path)
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origin = urljoin('file://', pathname2url(os.path.abspath(tar_file_path)))
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path = keras.utils.data_utils.get_file('test.txt', origin,
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untar=True, cache_subdir=dest_dir)
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filepath = path + '.tar.gz'
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hashval_sha256 = keras.utils.data_utils._hash_file(filepath)
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hashval_md5 = keras.utils.data_utils._hash_file(filepath, algorithm='md5')
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path = keras.utils.data_utils.get_file(
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'test.txt', origin, md5_hash=hashval_md5,
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untar=True, cache_subdir=dest_dir)
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path = keras.utils.data_utils.get_file(
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filepath, origin, file_hash=hashval_sha256,
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extract=True, cache_subdir=dest_dir)
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self.assertTrue(os.path.exists(filepath))
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self.assertTrue(keras.utils.data_utils.validate_file(filepath,
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hashval_sha256))
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self.assertTrue(keras.utils.data_utils.validate_file(filepath, hashval_md5))
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os.remove(filepath)
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origin = urljoin('file://', pathname2url(os.path.abspath(zip_file_path)))
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hashval_sha256 = keras.utils.data_utils._hash_file(zip_file_path)
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hashval_md5 = keras.utils.data_utils._hash_file(zip_file_path,
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algorithm='md5')
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path = keras.utils.data_utils.get_file(
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'test', origin, md5_hash=hashval_md5,
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extract=True, cache_subdir=dest_dir)
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path = keras.utils.data_utils.get_file(
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'test', origin, file_hash=hashval_sha256,
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extract=True, cache_subdir=dest_dir)
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self.assertTrue(os.path.exists(path))
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self.assertTrue(keras.utils.data_utils.validate_file(path, hashval_sha256))
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self.assertTrue(keras.utils.data_utils.validate_file(path, hashval_md5))
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class TestSequence(keras.utils.data_utils.Sequence):
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def __init__(self, shape, value=1.):
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self.shape = shape
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self.inner = value
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def __getitem__(self, item):
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return np.ones(self.shape, dtype=np.uint32) * item * self.inner
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def __len__(self):
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return 100
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def on_epoch_end(self):
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self.inner *= 5.0
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class FaultSequence(keras.utils.data_utils.Sequence):
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def __getitem__(self, item):
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raise IndexError(item, 'item is not present')
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def __len__(self):
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return 100
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@data_utils.threadsafe_generator
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def create_generator_from_sequence_threads(ds):
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for i in cycle(range(len(ds))):
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yield ds[i]
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def create_generator_from_sequence_pcs(ds):
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for i in cycle(range(len(ds))):
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yield ds[i]
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class TestEnqueuers(test.TestCase):
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def test_generator_enqueuer_threads(self):
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enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
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create_generator_from_sequence_threads(TestSequence([3, 200, 200, 3])),
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use_multiprocessing=False)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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acc = []
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for _ in range(100):
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acc.append(int(next(gen_output)[0, 0, 0, 0]))
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self.assertEqual(len(set(acc) - set(range(100))), 0)
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enqueuer.stop()
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@data_utils.dont_use_multiprocessing_pool
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def test_generator_enqueuer_processes(self):
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enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
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create_generator_from_sequence_threads(TestSequence([3, 200, 200, 3])),
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use_multiprocessing=True)
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enqueuer.start(4, 10)
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gen_output = enqueuer.get()
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acc = []
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for _ in range(300):
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acc.append(int(next(gen_output)[0, 0, 0, 0]))
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self.assertNotEqual(acc, list(range(100)))
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enqueuer.stop()
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def test_generator_enqueuer_fail_threads(self):
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enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
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create_generator_from_sequence_threads(FaultSequence()),
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use_multiprocessing=False)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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with self.assertRaises(IndexError):
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next(gen_output)
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@data_utils.dont_use_multiprocessing_pool
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def test_generator_enqueuer_fail_processes(self):
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enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
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create_generator_from_sequence_threads(FaultSequence()),
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use_multiprocessing=True)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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with self.assertRaises(IndexError):
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next(gen_output)
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def test_ordered_enqueuer_threads(self):
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enqueuer = keras.utils.data_utils.OrderedEnqueuer(
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TestSequence([3, 200, 200, 3]), use_multiprocessing=False)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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acc = []
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for _ in range(100):
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acc.append(next(gen_output)[0, 0, 0, 0])
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self.assertEqual(acc, list(range(100)))
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enqueuer.stop()
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@data_utils.dont_use_multiprocessing_pool
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def test_ordered_enqueuer_processes(self):
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enqueuer = keras.utils.data_utils.OrderedEnqueuer(
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TestSequence([3, 200, 200, 3]), use_multiprocessing=True)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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acc = []
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for _ in range(100):
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acc.append(next(gen_output)[0, 0, 0, 0])
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self.assertEqual(acc, list(range(100)))
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enqueuer.stop()
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def test_ordered_enqueuer_fail_threads(self):
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enqueuer = keras.utils.data_utils.OrderedEnqueuer(
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FaultSequence(), use_multiprocessing=False)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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with self.assertRaises(IndexError):
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next(gen_output)
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@data_utils.dont_use_multiprocessing_pool
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def test_ordered_enqueuer_fail_processes(self):
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enqueuer = keras.utils.data_utils.OrderedEnqueuer(
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FaultSequence(), use_multiprocessing=True)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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with self.assertRaises(IndexError):
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next(gen_output)
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@data_utils.dont_use_multiprocessing_pool
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def test_on_epoch_end_processes(self):
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enqueuer = keras.utils.data_utils.OrderedEnqueuer(
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TestSequence([3, 200, 200, 3]), use_multiprocessing=True)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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acc = []
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for _ in range(200):
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acc.append(next(gen_output)[0, 0, 0, 0])
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# Check that order was keep in GeneratorEnqueuer with processes
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self.assertEqual(acc[100:], list([k * 5 for k in range(100)]))
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enqueuer.stop()
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@data_utils.dont_use_multiprocessing_pool
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def test_context_switch(self):
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enqueuer = keras.utils.data_utils.OrderedEnqueuer(
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TestSequence([3, 200, 200, 3]), use_multiprocessing=True)
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enqueuer2 = keras.utils.data_utils.OrderedEnqueuer(
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TestSequence([3, 200, 200, 3], value=15), use_multiprocessing=True)
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enqueuer.start(3, 10)
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enqueuer2.start(3, 10)
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gen_output = enqueuer.get()
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gen_output2 = enqueuer2.get()
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acc = []
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for _ in range(100):
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acc.append(next(gen_output)[0, 0, 0, 0])
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self.assertEqual(acc[-1], 99)
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# One epoch is completed so enqueuer will switch the Sequence
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acc = []
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self.skipTest('b/145555807 flakily timing out.')
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for _ in range(100):
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acc.append(next(gen_output2)[0, 0, 0, 0])
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self.assertEqual(acc[-1], 99 * 15)
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# One epoch has been completed so enqueuer2 will switch
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# Be sure that both Sequence were updated
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self.assertEqual(next(gen_output)[0, 0, 0, 0], 0)
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self.assertEqual(next(gen_output)[0, 0, 0, 0], 5)
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self.assertEqual(next(gen_output2)[0, 0, 0, 0], 0)
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self.assertEqual(next(gen_output2)[0, 0, 0, 0], 15 * 5)
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# Tear down everything
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enqueuer.stop()
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enqueuer2.stop()
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def test_on_epoch_end_threads(self):
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enqueuer = keras.utils.data_utils.OrderedEnqueuer(
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TestSequence([3, 200, 200, 3]), use_multiprocessing=False)
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enqueuer.start(3, 10)
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gen_output = enqueuer.get()
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acc = []
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for _ in range(100):
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acc.append(next(gen_output)[0, 0, 0, 0])
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acc = []
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for _ in range(100):
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acc.append(next(gen_output)[0, 0, 0, 0])
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# Check that order was keep in GeneratorEnqueuer with processes
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self.assertEqual(acc, list([k * 5 for k in range(100)]))
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enqueuer.stop()
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if __name__ == '__main__':
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# Bazel sets these environment variables to very long paths.
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# Tempfile uses them to create long paths, and in turn multiprocessing
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# library tries to create sockets named after paths. Delete whatever bazel
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# writes to these to avoid tests failing due to socket addresses being too
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# long.
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for var in ('TMPDIR', 'TMP', 'TEMP'):
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if var in os.environ:
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del os.environ[var]
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test.main()
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