|
|
| from torch import Tensor
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|
|
| from mmaction.registry import MODELS
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| from .base import BaseRecognizer
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|
|
|
|
| @MODELS.register_module()
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| class RecognizerAudio(BaseRecognizer):
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| """Audio recognizer model framework."""
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|
|
| def extract_feat(self,
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| batch_inputs: Tensor,
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| stage: str = 'backbone',
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| **kwargs) -> tuple:
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| """Extract features of different stages.
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|
|
| Args:
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| batch_inputs (Tensor): The input data.
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| stage (str): Which stage to output the feature.
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| Defaults to ``backbone``.
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|
|
| Returns:
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| Tensor: The extracted features.
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| dict: A dict recording the kwargs for downstream
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| pipeline. This will be an empty dict in audio recognizer.
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| """
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|
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|
|
| loss_predict_kwargs = dict()
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| batch_inputs = batch_inputs.view((-1, ) + batch_inputs.shape[2:])
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|
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| x = self.backbone(batch_inputs)
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|
|
| if stage == 'backbone':
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| return x, loss_predict_kwargs
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|
|
| if self.with_cls_head and stage == 'head':
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| x = self.cls_head(x, **loss_predict_kwargs)
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| return x, loss_predict_kwargs
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|
|
