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| # coding=utf-8 | |
| # Copyright 2021 HuggingFace Inc. | |
| # | |
| # 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. | |
| import numpy as np | |
| from transformers import BatchFeature | |
| from transformers.testing_utils import require_tf, require_torch | |
| from .test_feature_extraction_common import FeatureExtractionSavingTestMixin | |
| class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin): | |
| # to overwrite at feature extractactor specific tests | |
| feat_extract_tester = None | |
| feature_extraction_class = None | |
| def feat_extract_dict(self): | |
| return self.feat_extract_tester.prepare_feat_extract_dict() | |
| def test_feat_extract_common_properties(self): | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| self.assertTrue(hasattr(feat_extract, "feature_size")) | |
| self.assertTrue(hasattr(feat_extract, "sampling_rate")) | |
| self.assertTrue(hasattr(feat_extract, "padding_value")) | |
| def test_batch_feature(self): | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common() | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| input_name = feat_extract.model_input_names[0] | |
| processed_features = BatchFeature({input_name: speech_inputs}) | |
| self.assertTrue(all(len(x) == len(y) for x, y in zip(speech_inputs, processed_features[input_name]))) | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True) | |
| processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="np") | |
| batch_features_input = processed_features[input_name] | |
| if len(batch_features_input.shape) < 3: | |
| batch_features_input = batch_features_input[:, :, None] | |
| self.assertTrue( | |
| batch_features_input.shape | |
| == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size) | |
| ) | |
| def test_batch_feature_pt(self): | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True) | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| input_name = feat_extract.model_input_names[0] | |
| processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="pt") | |
| batch_features_input = processed_features[input_name] | |
| if len(batch_features_input.shape) < 3: | |
| batch_features_input = batch_features_input[:, :, None] | |
| self.assertTrue( | |
| batch_features_input.shape | |
| == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size) | |
| ) | |
| def test_batch_feature_tf(self): | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True) | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| input_name = feat_extract.model_input_names[0] | |
| processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="tf") | |
| batch_features_input = processed_features[input_name] | |
| if len(batch_features_input.shape) < 3: | |
| batch_features_input = batch_features_input[:, :, None] | |
| self.assertTrue( | |
| batch_features_input.shape | |
| == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size) | |
| ) | |
| def _check_padding(self, numpify=False): | |
| def _inputs_have_equal_length(input): | |
| length = len(input[0]) | |
| for input_slice in input[1:]: | |
| if len(input_slice) != length: | |
| return False | |
| return True | |
| def _inputs_are_equal(input_1, input_2): | |
| if len(input_1) != len(input_2): | |
| return False | |
| for input_slice_1, input_slice_2 in zip(input_1, input_2): | |
| if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3): | |
| return False | |
| return True | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify) | |
| input_name = feat_extract.model_input_names[0] | |
| processed_features = BatchFeature({input_name: speech_inputs}) | |
| pad_diff = self.feat_extract_tester.seq_length_diff | |
| pad_max_length = self.feat_extract_tester.max_seq_length + pad_diff | |
| pad_min_length = self.feat_extract_tester.min_seq_length | |
| batch_size = self.feat_extract_tester.batch_size | |
| feature_size = self.feat_extract_tester.feature_size | |
| # test padding for List[int] + numpy | |
| input_1 = feat_extract.pad(processed_features, padding=False) | |
| input_1 = input_1[input_name] | |
| input_2 = feat_extract.pad(processed_features, padding="longest") | |
| input_2 = input_2[input_name] | |
| input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1])) | |
| input_3 = input_3[input_name] | |
| input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np") | |
| input_4 = input_4[input_name] | |
| # max_length parameter has to be provided when setting `padding="max_length"` | |
| with self.assertRaises(ValueError): | |
| feat_extract.pad(processed_features, padding="max_length")[input_name] | |
| input_5 = feat_extract.pad( | |
| processed_features, padding="max_length", max_length=pad_max_length, return_tensors="np" | |
| ) | |
| input_5 = input_5[input_name] | |
| self.assertFalse(_inputs_have_equal_length(input_1)) | |
| self.assertTrue(_inputs_have_equal_length(input_2)) | |
| self.assertTrue(_inputs_have_equal_length(input_3)) | |
| self.assertTrue(_inputs_are_equal(input_2, input_3)) | |
| self.assertTrue(len(input_1[0]) == pad_min_length) | |
| self.assertTrue(len(input_1[1]) == pad_min_length + pad_diff) | |
| self.assertTrue(input_4.shape[:2] == (batch_size, len(input_3[0]))) | |
| self.assertTrue(input_5.shape[:2] == (batch_size, pad_max_length)) | |
| if feature_size > 1: | |
| self.assertTrue(input_4.shape[2] == input_5.shape[2] == feature_size) | |
| # test padding for `pad_to_multiple_of` for List[int] + numpy | |
| input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10) | |
| input_6 = input_6[input_name] | |
| input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10) | |
| input_7 = input_7[input_name] | |
| input_8 = feat_extract.pad( | |
| processed_features, padding="max_length", pad_to_multiple_of=10, max_length=pad_max_length | |
| ) | |
| input_8 = input_8[input_name] | |
| input_9 = feat_extract.pad( | |
| processed_features, | |
| padding="max_length", | |
| pad_to_multiple_of=10, | |
| max_length=pad_max_length, | |
| return_tensors="np", | |
| ) | |
| input_9 = input_9[input_name] | |
| self.assertTrue(all(len(x) % 10 == 0 for x in input_6)) | |
| self.assertTrue(_inputs_are_equal(input_6, input_7)) | |
| expected_mult_pad_length = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10 | |
| self.assertTrue(all(len(x) == expected_mult_pad_length for x in input_8)) | |
| self.assertEqual(input_9.shape[:2], (batch_size, expected_mult_pad_length)) | |
| if feature_size > 1: | |
| self.assertTrue(input_9.shape[2] == feature_size) | |
| # Check padding value is correct | |
| padding_vector_sum = (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum() | |
| self.assertTrue( | |
| abs(np.asarray(input_2[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) | |
| < 1e-3 | |
| ) | |
| self.assertTrue( | |
| abs( | |
| np.asarray(input_2[1])[pad_min_length + pad_diff :].sum() | |
| - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) | |
| ) | |
| < 1e-3 | |
| ) | |
| self.assertTrue( | |
| abs( | |
| np.asarray(input_2[2])[pad_min_length + 2 * pad_diff :].sum() | |
| - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) | |
| ) | |
| < 1e-3 | |
| ) | |
| self.assertTrue( | |
| abs(input_5[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3 | |
| ) | |
| self.assertTrue( | |
| abs(input_9[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)) | |
| < 1e-3 | |
| ) | |
| def _check_truncation(self, numpify=False): | |
| def _inputs_have_equal_length(input): | |
| length = len(input[0]) | |
| for input_slice in input[1:]: | |
| if len(input_slice) != length: | |
| return False | |
| return True | |
| def _inputs_are_equal(input_1, input_2): | |
| if len(input_1) != len(input_2): | |
| return False | |
| for input_slice_1, input_slice_2 in zip(input_1, input_2): | |
| if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3): | |
| return False | |
| return True | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify) | |
| input_name = feat_extract.model_input_names[0] | |
| processed_features = BatchFeature({input_name: speech_inputs}) | |
| # truncate to smallest | |
| input_1 = feat_extract.pad( | |
| processed_features, padding="max_length", max_length=len(speech_inputs[0]), truncation=True | |
| ) | |
| input_1 = input_1[input_name] | |
| input_2 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[0])) | |
| input_2 = input_2[input_name] | |
| self.assertTrue(_inputs_have_equal_length(input_1)) | |
| self.assertFalse(_inputs_have_equal_length(input_2)) | |
| # truncate to smallest with np | |
| input_3 = feat_extract.pad( | |
| processed_features, | |
| padding="max_length", | |
| max_length=len(speech_inputs[0]), | |
| return_tensors="np", | |
| truncation=True, | |
| ) | |
| input_3 = input_3[input_name] | |
| input_4 = feat_extract.pad( | |
| processed_features, padding="max_length", max_length=len(speech_inputs[0]), return_tensors="np" | |
| ) | |
| input_4 = input_4[input_name] | |
| self.assertTrue(_inputs_have_equal_length(input_3)) | |
| self.assertTrue(input_3.shape[1] == len(speech_inputs[0])) | |
| # since truncation forces padding to be smaller than longest input | |
| # function can't return `np.ndarray`, but has to return list | |
| self.assertFalse(_inputs_have_equal_length(input_4)) | |
| # truncate to middle | |
| input_5 = feat_extract.pad( | |
| processed_features, | |
| padding="max_length", | |
| max_length=len(speech_inputs[1]), | |
| truncation=True, | |
| return_tensors="np", | |
| ) | |
| input_5 = input_5[input_name] | |
| input_6 = feat_extract.pad( | |
| processed_features, padding="max_length", max_length=len(speech_inputs[1]), truncation=True | |
| ) | |
| input_6 = input_6[input_name] | |
| input_7 = feat_extract.pad( | |
| processed_features, padding="max_length", max_length=len(speech_inputs[1]), return_tensors="np" | |
| ) | |
| input_7 = input_7[input_name] | |
| self.assertTrue(input_5.shape[1] == len(speech_inputs[1])) | |
| self.assertTrue(_inputs_have_equal_length(input_5)) | |
| self.assertTrue(_inputs_have_equal_length(input_6)) | |
| self.assertTrue(_inputs_are_equal(input_5, input_6)) | |
| # since truncation forces padding to be smaller than longest input | |
| # function can't return `np.ndarray`, but has to return list | |
| self.assertFalse(_inputs_have_equal_length(input_7)) | |
| self.assertTrue(len(input_7[-1]) == len(speech_inputs[-1])) | |
| # padding has to be max_length when setting `truncation=True` | |
| with self.assertRaises(ValueError): | |
| feat_extract.pad(processed_features, truncation=True)[input_name] | |
| # padding has to be max_length when setting `truncation=True` | |
| with self.assertRaises(ValueError): | |
| feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name] | |
| # padding has to be max_length when setting `truncation=True` | |
| with self.assertRaises(ValueError): | |
| feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name] | |
| # max_length parameter has to be provided when setting `truncation=True` and padding="max_length" | |
| with self.assertRaises(ValueError): | |
| feat_extract.pad(processed_features, padding="max_length", truncation=True)[input_name] | |
| # test truncation for `pad_to_multiple_of` for List[int] + numpy | |
| pad_to_multiple_of = 12 | |
| input_8 = feat_extract.pad( | |
| processed_features, | |
| padding="max_length", | |
| max_length=len(speech_inputs[0]), | |
| pad_to_multiple_of=pad_to_multiple_of, | |
| truncation=True, | |
| ) | |
| input_8 = input_8[input_name] | |
| input_9 = feat_extract.pad( | |
| processed_features, | |
| padding="max_length", | |
| max_length=len(speech_inputs[0]), | |
| pad_to_multiple_of=pad_to_multiple_of, | |
| ) | |
| input_9 = input_9[input_name] | |
| # retrieve expected_length as multiple of pad_to_multiple_of | |
| expected_length = len(speech_inputs[0]) | |
| if expected_length % pad_to_multiple_of != 0: | |
| expected_length = ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of | |
| self.assertTrue(len(input_8[0]) == expected_length) | |
| self.assertTrue(_inputs_have_equal_length(input_8)) | |
| self.assertFalse(_inputs_have_equal_length(input_9)) | |
| def test_padding_from_list(self): | |
| self._check_padding(numpify=False) | |
| def test_padding_from_array(self): | |
| self._check_padding(numpify=True) | |
| def test_truncation_from_list(self): | |
| self._check_truncation(numpify=False) | |
| def test_truncation_from_array(self): | |
| self._check_truncation(numpify=True) | |
| def test_padding_accepts_tensors_pt(self): | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common() | |
| input_name = feat_extract.model_input_names[0] | |
| processed_features = BatchFeature({input_name: speech_inputs}) | |
| input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name] | |
| input_pt = feat_extract.pad(processed_features, padding="longest", return_tensors="pt")[input_name] | |
| self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2) | |
| def test_padding_accepts_tensors_tf(self): | |
| feat_extract = self.feature_extraction_class(**self.feat_extract_dict) | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common() | |
| input_name = feat_extract.model_input_names[0] | |
| processed_features = BatchFeature({input_name: speech_inputs}) | |
| input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name] | |
| input_tf = feat_extract.pad(processed_features, padding="longest", return_tensors="tf")[input_name] | |
| self.assertTrue(abs(input_np.astype(np.float32).sum() - input_tf.numpy().astype(np.float32).sum()) < 1e-2) | |
| def test_attention_mask(self): | |
| feat_dict = self.feat_extract_dict | |
| feat_dict["return_attention_mask"] = True | |
| feat_extract = self.feature_extraction_class(**feat_dict) | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common() | |
| input_lengths = [len(x) for x in speech_inputs] | |
| input_name = feat_extract.model_input_names[0] | |
| processed = BatchFeature({input_name: speech_inputs}) | |
| processed = feat_extract.pad(processed, padding="longest", return_tensors="np") | |
| self.assertIn("attention_mask", processed) | |
| self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2])) | |
| self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lengths) | |
| def test_attention_mask_with_truncation(self): | |
| feat_dict = self.feat_extract_dict | |
| feat_dict["return_attention_mask"] = True | |
| feat_extract = self.feature_extraction_class(**feat_dict) | |
| speech_inputs = self.feat_extract_tester.prepare_inputs_for_common() | |
| input_lengths = [len(x) for x in speech_inputs] | |
| input_name = feat_extract.model_input_names[0] | |
| processed = BatchFeature({input_name: speech_inputs}) | |
| max_length = min(input_lengths) | |
| processed_pad = feat_extract.pad( | |
| processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np" | |
| ) | |
| self.assertIn("attention_mask", processed_pad) | |
| self.assertListEqual( | |
| list(processed_pad.attention_mask.shape), [processed_pad[input_name].shape[0], max_length] | |
| ) | |
| self.assertListEqual( | |
| processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs] | |
| ) | |