bcdbfb8a9c
- Display number of files used for training/validation when validation_split is used - Refuse to perform validation split if the data is shuffled and not seeded PiperOrigin-RevId: 308750122 Change-Id: I07f9090e714d1290532c7b7b7f51417f7193c797
229 lines
8.8 KiB
Python
229 lines
8.8 KiB
Python
# Copyright 2020 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 text_dataset."""
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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 random
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import shutil
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import string
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from tensorflow.python.compat import v2_compat
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras.preprocessing import text_dataset
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from tensorflow.python.platform import test
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class TextDatasetFromDirectoryTest(keras_parameterized.TestCase):
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def _prepare_directory(self,
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num_classes=2,
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nested_dirs=False,
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count=16,
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length=20):
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# Get a unique temp directory
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temp_dir = os.path.join(self.get_temp_dir(), str(random.randint(0, 1e6)))
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os.mkdir(temp_dir)
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self.addCleanup(shutil.rmtree, temp_dir)
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# Generate paths to class subdirectories
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paths = []
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for class_index in range(num_classes):
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class_directory = 'class_%s' % (class_index,)
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if nested_dirs:
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class_paths = [
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class_directory, os.path.join(class_directory, 'subfolder_1'),
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os.path.join(class_directory, 'subfolder_2'), os.path.join(
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class_directory, 'subfolder_1', 'sub-subfolder')
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]
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else:
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class_paths = [class_directory]
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for path in class_paths:
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os.mkdir(os.path.join(temp_dir, path))
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paths += class_paths
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for i in range(count):
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path = paths[count % len(paths)]
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filename = os.path.join(path, 'text_%s.txt' % (i,))
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f = open(os.path.join(temp_dir, filename), 'w')
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text = ''.join([random.choice(string.printable) for _ in range(length)])
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f.write(text)
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f.close()
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return temp_dir
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def test_text_dataset_from_directory_binary(self):
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directory = self._prepare_directory(num_classes=2)
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode='int', max_length=10)
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertEqual(batch[0].shape, (8,))
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self.assertEqual(batch[0].dtype.name, 'string')
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self.assertEqual(len(batch[0].numpy()[0]), 10) # Test max_length
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self.assertEqual(batch[1].shape, (8,))
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self.assertEqual(batch[1].dtype.name, 'int32')
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode='binary')
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertEqual(batch[0].shape, (8,))
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self.assertEqual(batch[0].dtype.name, 'string')
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self.assertEqual(batch[1].shape, (8, 1))
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self.assertEqual(batch[1].dtype.name, 'float32')
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode='categorical')
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertEqual(batch[0].shape, (8,))
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self.assertEqual(batch[0].dtype.name, 'string')
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self.assertEqual(batch[1].shape, (8, 2))
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self.assertEqual(batch[1].dtype.name, 'float32')
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def test_sample_count(self):
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directory = self._prepare_directory(num_classes=4, count=15)
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode=None)
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sample_count = 0
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for batch in dataset:
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sample_count += batch.shape[0]
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self.assertEqual(sample_count, 15)
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def test_text_dataset_from_directory_multiclass(self):
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directory = self._prepare_directory(num_classes=4, count=15)
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode=None)
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batch = next(iter(dataset))
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self.assertEqual(batch.shape, (8,))
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode=None)
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sample_count = 0
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iterator = iter(dataset)
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for batch in dataset:
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sample_count += next(iterator).shape[0]
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self.assertEqual(sample_count, 15)
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode='int')
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertEqual(batch[0].shape, (8,))
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self.assertEqual(batch[0].dtype.name, 'string')
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self.assertEqual(batch[1].shape, (8,))
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self.assertEqual(batch[1].dtype.name, 'int32')
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode='categorical')
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertEqual(batch[0].shape, (8,))
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self.assertEqual(batch[0].dtype.name, 'string')
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self.assertEqual(batch[1].shape, (8, 4))
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self.assertEqual(batch[1].dtype.name, 'float32')
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def test_text_dataset_from_directory_validation_split(self):
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directory = self._prepare_directory(num_classes=2, count=10)
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=10, validation_split=0.2, subset='training',
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seed=1337)
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertEqual(batch[0].shape, (8,))
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=10, validation_split=0.2, subset='validation',
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seed=1337)
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertEqual(batch[0].shape, (2,))
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def test_text_dataset_from_directory_manual_labels(self):
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directory = self._prepare_directory(num_classes=2, count=2)
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, labels=[0, 1], shuffle=False)
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batch = next(iter(dataset))
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self.assertLen(batch, 2)
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self.assertAllClose(batch[1], [0, 1])
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def test_text_dataset_from_directory_follow_links(self):
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directory = self._prepare_directory(num_classes=2, count=25,
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nested_dirs=True)
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dataset = text_dataset.text_dataset_from_directory(
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directory, batch_size=8, label_mode=None, follow_links=True)
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sample_count = 0
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for batch in dataset:
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sample_count += batch.shape[0]
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self.assertEqual(sample_count, 25)
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def test_text_dataset_from_directory_errors(self):
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directory = self._prepare_directory(num_classes=3, count=5)
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with self.assertRaisesRegex(ValueError, '`labels` argument should be'):
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_ = text_dataset.text_dataset_from_directory(
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directory, labels=None)
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with self.assertRaisesRegex(ValueError, '`label_mode` argument must be'):
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_ = text_dataset.text_dataset_from_directory(
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directory, label_mode='other')
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with self.assertRaisesRegex(
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ValueError, 'only pass `class_names` if the labels are inferred'):
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_ = text_dataset.text_dataset_from_directory(
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directory, labels=[0, 0, 1, 1, 1],
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class_names=['class_0', 'class_1', 'class_2'])
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with self.assertRaisesRegex(
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ValueError,
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'Expected the lengths of `labels` to match the number of files'):
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_ = text_dataset.text_dataset_from_directory(
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directory, labels=[0, 0, 1, 1])
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with self.assertRaisesRegex(
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ValueError, '`class_names` passed did not match'):
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_ = text_dataset.text_dataset_from_directory(
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directory, class_names=['class_0', 'class_2'])
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with self.assertRaisesRegex(ValueError, 'there must exactly 2 classes'):
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_ = text_dataset.text_dataset_from_directory(
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directory, label_mode='binary')
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with self.assertRaisesRegex(ValueError,
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'`validation_split` must be between 0 and 1'):
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_ = text_dataset.text_dataset_from_directory(
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directory, validation_split=2)
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with self.assertRaisesRegex(ValueError,
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'`subset` must be either "training" or'):
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_ = text_dataset.text_dataset_from_directory(
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directory, validation_split=0.2, subset='other')
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with self.assertRaisesRegex(ValueError, '`validation_split` must be set'):
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_ = text_dataset.text_dataset_from_directory(
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directory, validation_split=0, subset='training')
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with self.assertRaisesRegex(ValueError, 'must provide a `seed`'):
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_ = text_dataset.text_dataset_from_directory(
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directory, validation_split=0.2, subset='training')
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if __name__ == '__main__':
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v2_compat.enable_v2_behavior()
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test.main()
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