# 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 @property 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) ) @require_torch 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) ) @require_tf 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) @require_torch 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) @require_tf 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] )