76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
# Copyright 2022 The MediaPipe 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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import csv
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import os
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import tensorflow as tf
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from mediapipe.model_maker.python.text.text_classifier import dataset
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class DatasetTest(tf.test.TestCase):
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def _get_csv_file(self):
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labels_and_text = (('neutral', 'indifferent'), ('pos', 'extremely great'),
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('neg', 'totally awful'), ('pos', 'super good'),
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('neg', 'really bad'))
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csv_file = os.path.join(self.get_temp_dir(), 'data.csv')
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if os.path.exists(csv_file):
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return csv_file
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fieldnames = ['text', 'label']
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with open(csv_file, 'w') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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for label, text in labels_and_text:
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writer.writerow({'text': text, 'label': label})
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return csv_file
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def test_from_csv(self):
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csv_file = self._get_csv_file()
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csv_params = dataset.CSVParameters(text_column='text', label_column='label')
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data = dataset.Dataset.from_csv(filename=csv_file, csv_params=csv_params)
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self.assertLen(data, 5)
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self.assertEqual(data.num_classes, 3)
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self.assertEqual(data.label_names, ['neg', 'neutral', 'pos'])
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data_values = set([(text.numpy()[0], label.numpy()[0])
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for text, label in data.gen_tf_dataset()])
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expected_data_values = set([(b'indifferent', 1), (b'extremely great', 2),
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(b'totally awful', 0), (b'super good', 2),
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(b'really bad', 0)])
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self.assertEqual(data_values, expected_data_values)
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def test_split(self):
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ds = tf.data.Dataset.from_tensor_slices(['good', 'bad', 'neutral', 'odd'])
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data = dataset.Dataset(ds, 4, ['pos', 'neg'])
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train_data, test_data = data.split(0.5)
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expected_train_data = [b'good', b'bad']
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expected_test_data = [b'neutral', b'odd']
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self.assertLen(train_data, 2)
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train_data_values = [elem.numpy() for elem in train_data._dataset]
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self.assertEqual(train_data_values, expected_train_data)
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self.assertEqual(train_data.num_classes, 2)
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self.assertEqual(train_data.label_names, ['pos', 'neg'])
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self.assertLen(test_data, 2)
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test_data_values = [elem.numpy() for elem in test_data._dataset]
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self.assertEqual(test_data_values, expected_test_data)
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self.assertEqual(test_data.num_classes, 2)
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self.assertEqual(test_data.label_names, ['pos', 'neg'])
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if __name__ == '__main__':
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tf.test.main()
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