[Sequence Feature Extraction] Add truncation (#12804)
* fix_torch_device_generate_test * remove @ * add truncate * finish * correct test * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * clean tests * correct normalization for truncation * remove casting * up * save intermed * finish * finish * correct Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Patrick von Platen
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GitHub
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@@ -91,6 +91,7 @@ class Speech2TextFeatureExtractionTester(unittest.TestCase):
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if equal_length:
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speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
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else:
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# make sure that inputs increase in size
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speech_inputs = [
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floats_list((x, self.feature_size))
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for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
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@@ -147,3 +148,26 @@ class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
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_check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
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_check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
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_check_zero_mean_unit_variance(input_features[2, : fbank_feat_lengths[2]])
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def test_cepstral_mean_and_variance_normalization_trunc(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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inputs = feature_extractor(
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speech_inputs,
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padding="max_length",
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max_length=4,
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truncation=True,
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return_tensors="np",
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return_attention_mask=True,
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)
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input_features = inputs.input_features
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attention_mask = inputs.attention_mask
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fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
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def _check_zero_mean_unit_variance(input_vector):
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self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
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self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
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_check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
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_check_zero_mean_unit_variance(input_features[1])
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_check_zero_mean_unit_variance(input_features[2])
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@@ -83,6 +83,7 @@ class Wav2Vec2FeatureExtractionTester(unittest.TestCase):
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if equal_length:
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speech_inputs = floats_list((self.batch_size, self.max_seq_length))
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else:
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# make sure that inputs increase in size
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speech_inputs = [
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_flatten(floats_list((x, self.feature_size)))
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for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
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@@ -122,7 +123,7 @@ class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest
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def test_zero_mean_unit_variance_normalization(self):
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feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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processed = feat_extract(speech_inputs, padding="longest")
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processed = feat_extract(speech_inputs, padding="longest", return_tensors="np")
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input_values = processed.input_values
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def _check_zero_mean_unit_variance(input_vector):
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@@ -133,6 +134,22 @@ class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest
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_check_zero_mean_unit_variance(input_values[1, :1000])
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_check_zero_mean_unit_variance(input_values[2])
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def test_zero_mean_unit_variance_normalization_trunc(self):
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feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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processed = feat_extract(
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speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np"
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)
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input_values = processed.input_values
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def _check_zero_mean_unit_variance(input_vector):
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self.assertTrue(np.abs(np.mean(input_vector)) < 1e-3)
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self.assertTrue(np.abs(np.var(input_vector) - 1) < 1e-3)
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_check_zero_mean_unit_variance(input_values[0, :800])
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_check_zero_mean_unit_variance(input_values[1])
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_check_zero_mean_unit_variance(input_values[2])
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@slow
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@require_torch
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def test_pretrained_checkpoints_are_set_correctly(self):
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@@ -715,7 +715,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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input_speech = self._load_datasamples(2)
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inputs = processor(input_speech, return_tensors="pt", padding=True, truncation=True)
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inputs = processor(input_speech, return_tensors="pt", padding=True)
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input_values = inputs.input_values.to(torch_device)
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@@ -737,7 +737,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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input_speech = self._load_datasamples(4)
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inputs = processor(input_speech, return_tensors="pt", padding=True, truncation=True)
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inputs = processor(input_speech, return_tensors="pt", padding=True)
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input_values = inputs.input_values.to(torch_device)
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attention_mask = inputs.attention_mask.to(torch_device)
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@@ -126,12 +126,17 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
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feature_size = self.feat_extract_tester.feature_size
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# test padding for List[int] + numpy
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input_1 = feat_extract.pad(processed_features, padding=False)[input_name]
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input_2 = feat_extract.pad(processed_features, padding="longest")[input_name]
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input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1]))[
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input_name
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]
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input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
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input_1 = feat_extract.pad(processed_features, padding=False)
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input_1 = input_1[input_name]
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input_2 = feat_extract.pad(processed_features, padding="longest")
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input_2 = input_2[input_name]
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input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1]))
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input_3 = input_3[input_name]
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input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np")
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input_4 = input_4[input_name]
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# max_length parameter has to be provided when setting `padding="max_length"`
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with self.assertRaises(ValueError):
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@@ -139,7 +144,8 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
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input_5 = feat_extract.pad(
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processed_features, padding="max_length", max_length=pad_max_length, return_tensors="np"
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)[input_name]
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)
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input_5 = input_5[input_name]
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self.assertFalse(_inputs_have_equal_length(input_1))
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self.assertTrue(_inputs_have_equal_length(input_2))
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@@ -154,18 +160,25 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
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self.assertTrue(input_4.shape[2] == input_5.shape[2] == feature_size)
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# test padding for `pad_to_multiple_of` for List[int] + numpy
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input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)[input_name]
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input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10)[input_name]
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input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)
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input_6 = input_6[input_name]
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input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10)
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input_7 = input_7[input_name]
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input_8 = feat_extract.pad(
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processed_features, padding="max_length", pad_to_multiple_of=10, max_length=pad_max_length
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)[input_name]
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)
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input_8 = input_8[input_name]
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input_9 = feat_extract.pad(
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processed_features,
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padding="max_length",
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pad_to_multiple_of=10,
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max_length=pad_max_length,
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return_tensors="np",
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)[input_name]
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)
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input_9 = input_9[input_name]
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self.assertTrue(all(len(x) % 10 == 0 for x in input_6))
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self.assertTrue(_inputs_are_equal(input_6, input_7))
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@@ -205,12 +218,149 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
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< 1e-3
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)
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def _check_truncation(self, numpify=False):
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def _inputs_have_equal_length(input):
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length = len(input[0])
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for input_slice in input[1:]:
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if len(input_slice) != length:
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return False
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return True
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def _inputs_are_equal(input_1, input_2):
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if len(input_1) != len(input_2):
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return False
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for input_slice_1, input_slice_2 in zip(input_1, input_2):
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if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
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return False
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return True
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feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
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speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
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input_name = feat_extract.model_input_names[0]
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processed_features = BatchFeature({input_name: speech_inputs})
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# truncate to smallest
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input_1 = feat_extract.pad(
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processed_features, padding="max_length", max_length=len(speech_inputs[0]), truncation=True
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)
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input_1 = input_1[input_name]
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input_2 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[0]))
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input_2 = input_2[input_name]
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self.assertTrue(_inputs_have_equal_length(input_1))
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self.assertFalse(_inputs_have_equal_length(input_2))
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# truncate to smallest with np
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input_3 = feat_extract.pad(
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processed_features,
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padding="max_length",
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max_length=len(speech_inputs[0]),
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return_tensors="np",
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truncation=True,
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)
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input_3 = input_3[input_name]
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input_4 = feat_extract.pad(
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processed_features, padding="max_length", max_length=len(speech_inputs[0]), return_tensors="np"
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)
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input_4 = input_4[input_name]
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self.assertTrue(_inputs_have_equal_length(input_3))
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self.assertTrue(input_3.shape[1] == len(speech_inputs[0]))
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# since truncation forces padding to be smaller than longest input
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# function can't return `np.ndarray`, but has to return list
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self.assertFalse(_inputs_have_equal_length(input_4))
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# truncate to middle
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input_5 = feat_extract.pad(
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processed_features,
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padding="max_length",
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max_length=len(speech_inputs[1]),
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truncation=True,
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return_tensors="np",
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)
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input_5 = input_5[input_name]
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input_6 = feat_extract.pad(
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processed_features, padding="max_length", max_length=len(speech_inputs[1]), truncation=True
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)
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input_6 = input_6[input_name]
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input_7 = feat_extract.pad(
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processed_features, padding="max_length", max_length=len(speech_inputs[1]), return_tensors="np"
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)
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input_7 = input_7[input_name]
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self.assertTrue(input_5.shape[1] == len(speech_inputs[1]))
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self.assertTrue(_inputs_have_equal_length(input_5))
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self.assertTrue(_inputs_have_equal_length(input_6))
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self.assertTrue(_inputs_are_equal(input_5, input_6))
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# since truncation forces padding to be smaller than longest input
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# function can't return `np.ndarray`, but has to return list
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self.assertFalse(_inputs_have_equal_length(input_7))
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self.assertTrue(len(input_7[-1]) == len(speech_inputs[-1]))
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# padding has to be max_length when setting `truncation=True`
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with self.assertRaises(ValueError):
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feat_extract.pad(processed_features, truncation=True)[input_name]
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# padding has to be max_length when setting `truncation=True`
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with self.assertRaises(ValueError):
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feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]
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# padding has to be max_length when setting `truncation=True`
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with self.assertRaises(ValueError):
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feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]
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# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
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with self.assertRaises(ValueError):
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feat_extract.pad(processed_features, padding="max_length", truncation=True)[input_name]
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# test truncation for `pad_to_multiple_of` for List[int] + numpy
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pad_to_multiple_of = 12
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input_8 = feat_extract.pad(
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processed_features,
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padding="max_length",
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max_length=len(speech_inputs[0]),
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pad_to_multiple_of=pad_to_multiple_of,
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truncation=True,
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)
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input_8 = input_8[input_name]
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input_9 = feat_extract.pad(
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processed_features,
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padding="max_length",
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max_length=len(speech_inputs[0]),
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pad_to_multiple_of=pad_to_multiple_of,
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)
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input_9 = input_9[input_name]
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# retrieve expected_length as multiple of pad_to_multiple_of
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expected_length = len(speech_inputs[0])
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if expected_length % pad_to_multiple_of != 0:
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expected_length = ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of
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self.assertTrue(len(input_8[0]) == expected_length)
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self.assertTrue(_inputs_have_equal_length(input_8))
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self.assertFalse(_inputs_have_equal_length(input_9))
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def test_padding_from_list(self):
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self._check_padding(numpify=False)
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def test_padding_from_array(self):
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self._check_padding(numpify=True)
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def test_truncation_from_list(self):
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self._check_truncation(numpify=False)
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def test_truncation_from_array(self):
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self._check_truncation(numpify=True)
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@require_torch
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def test_padding_accepts_tensors_pt(self):
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feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
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@@ -251,3 +401,25 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
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self.assertIn("attention_mask", processed)
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self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
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self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)
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def test_attention_mask_with_truncation(self):
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feat_dict = self.feat_extract_dict
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feat_dict["return_attention_mask"] = True
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feat_extract = self.feature_extraction_class(**feat_dict)
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speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
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input_lenghts = [len(x) for x in speech_inputs]
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input_name = feat_extract.model_input_names[0]
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processed = BatchFeature({input_name: speech_inputs})
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max_length = min(input_lenghts)
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processed_pad = feat_extract.pad(
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processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"
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)
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self.assertIn("attention_mask", processed_pad)
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self.assertListEqual(
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list(processed_pad.attention_mask.shape), list((processed_pad[input_name].shape[0], max_length))
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)
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self.assertListEqual(
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processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs]
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)
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