| # Lint as: python3 |
| # Copyright 2019 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. |
| # ============================================================================== |
| # pylint: disable=g-bad-import-order |
| |
| """Test for data_load.py.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import unittest |
| from data_load import DataLoader |
| |
| import tensorflow as tf |
| |
| |
| class TestLoad(unittest.TestCase): |
| |
| def setUp(self): # pylint: disable=g-missing-super-call |
| self.loader = DataLoader( |
| "./data/train", "./data/valid", "./data/test", seq_length=512) |
| |
| def test_get_data(self): |
| self.assertIsInstance(self.loader.train_data, list) |
| self.assertIsInstance(self.loader.train_label, list) |
| self.assertIsInstance(self.loader.valid_data, list) |
| self.assertIsInstance(self.loader.valid_label, list) |
| self.assertIsInstance(self.loader.test_data, list) |
| self.assertIsInstance(self.loader.test_label, list) |
| self.assertEqual(self.loader.train_len, len(self.loader.train_data)) |
| self.assertEqual(self.loader.train_len, len(self.loader.train_label)) |
| self.assertEqual(self.loader.valid_len, len(self.loader.valid_data)) |
| self.assertEqual(self.loader.valid_len, len(self.loader.valid_label)) |
| self.assertEqual(self.loader.test_len, len(self.loader.test_data)) |
| self.assertEqual(self.loader.test_len, len(self.loader.test_label)) |
| |
| def test_pad(self): |
| original_data1 = [[2, 3], [1, 1]] |
| expected_data1_0 = [[2, 3], [2, 3], [2, 3], [2, 3], [1, 1]] |
| expected_data1_1 = [[2, 3], [1, 1], [1, 1], [1, 1], [1, 1]] |
| original_data2 = [[-2, 3], [-77, -681], [5, 6], [9, -7], [22, 3333], |
| [9, 99], [-100, 0]] |
| expected_data2 = [[-2, 3], [-77, -681], [5, 6], [9, -7], [22, 3333]] |
| padding_data1 = self.loader.pad(original_data1, seq_length=5, dim=2) |
| padding_data2 = self.loader.pad(original_data2, seq_length=5, dim=2) |
| for i in range(len(padding_data1[0])): |
| for j in range(len(padding_data1[0].tolist()[0])): |
| self.assertLess( |
| abs(padding_data1[0].tolist()[i][j] - expected_data1_0[i][j]), |
| 10.001) |
| for i in range(len(padding_data1[1])): |
| for j in range(len(padding_data1[1].tolist()[0])): |
| self.assertLess( |
| abs(padding_data1[1].tolist()[i][j] - expected_data1_1[i][j]), |
| 10.001) |
| self.assertEqual(padding_data2[0].tolist(), expected_data2) |
| self.assertEqual(padding_data2[1].tolist(), expected_data2) |
| |
| def test_format(self): |
| self.loader.format() |
| expected_train_label = int(self.loader.label2id[self.loader.train_label[0]]) |
| expected_valid_label = int(self.loader.label2id[self.loader.valid_label[0]]) |
| expected_test_label = int(self.loader.label2id[self.loader.test_label[0]]) |
| for feature, label in self.loader.train_data: # pylint: disable=unused-variable |
| format_train_label = label.numpy() |
| break |
| for feature, label in self.loader.valid_data: |
| format_valid_label = label.numpy() |
| break |
| for feature, label in self.loader.test_data: |
| format_test_label = label.numpy() |
| break |
| self.assertEqual(expected_train_label, format_train_label) |
| self.assertEqual(expected_valid_label, format_valid_label) |
| self.assertEqual(expected_test_label, format_test_label) |
| self.assertIsInstance(self.loader.train_data, tf.data.Dataset) |
| self.assertIsInstance(self.loader.valid_data, tf.data.Dataset) |
| self.assertIsInstance(self.loader.test_data, tf.data.Dataset) |
| |
| |
| if __name__ == "__main__": |
| unittest.main() |
