[Wav2Vec2FeatureExtractor] Fix extractor.pad() dtype backwards compatibility
#13693
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anton-l
commented
Sep 22, 2021
| @@ -187,23 +187,6 @@ def pad( | |||
| padding_strategy = self._get_padding_strategies(padding=padding, max_length=max_length) | |||
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| required_input = processed_features[self.model_input_names[0]] | |||
| if required_input and not isinstance(required_input[0], np.ndarray): | |||
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Leftover logic from the old implementation, required_input[0] is always np.ndarray now
anton-l
commented
Sep 22, 2021
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| if value.dtype is np.dtype(np.float64): | ||
| value = value.astype(np.float32) |
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Instead of adding dtype customization to .pad(), just always use np.float32, until we really need float64 support for some new models.
patrickvonplaten
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Sep 22, 2021
Narsil
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Sep 25, 2021
…atibility (huggingface#13693) * Force dtype, add tests * Local torch imports * Remove unused logic (always ndarray)
stas00
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Oct 12, 2021
…atibility (huggingface#13693) * Force dtype, add tests * Local torch imports * Remove unused logic (always ndarray)
Albertobegue
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Jan 13, 2022
…atibility (huggingface#13693) * Force dtype, add tests * Local torch imports * Remove unused logic (always ndarray)
Albertobegue
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Jan 27, 2022
…atibility (huggingface#13693) * Force dtype, add tests * Local torch imports * Remove unused logic (always ndarray)
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Resolves #13689
This fixes an issue introduced by #13650 with speech feature extractors' tensors being returned as torch.float64 when
.pad()is called directly:This is due to how pytorch converts float numpy arrays (new padding logic) vs python lists (old padding logic):
torch.tensor([1.2, 2.3]).dtype # torch.float32np.array([1.2, 2.3]).dtype # np.float64torch.tensor(np.array([1.2, 2.3])).dtype # torch.float64