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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Tuple
import torch
from mmaction.registry import MODELS
from mmaction.utils import OptSampleList
from .base import BaseRecognizer
@MODELS.register_module()
class MMRecognizer3D(BaseRecognizer):
"""Multi-modal 3D recognizer model framework."""
def extract_feat(self,
inputs: Dict[str, torch.Tensor],
stage: str = 'backbone',
data_samples: OptSampleList = None,
test_mode: bool = False) -> Tuple:
"""Extract features.
Args:
inputs (dict[str, torch.Tensor]): The multi-modal input data.
stage (str): Which stage to output the feature.
Defaults to ``'backbone'``.
data_samples (list[:obj:`ActionDataSample`], optional): Action data
samples, which are only needed in training. Defaults to None.
test_mode (bool): Whether in test mode. Defaults to False.
Returns:
tuple[torch.Tensor]: The extracted features.
dict: A dict recording the kwargs for downstream
pipeline.
"""
# [N, num_views, C, T, H, W] ->
# [N * num_views, C, T, H, W]
for m, m_data in inputs.items():
m_data = m_data.reshape((-1, ) + m_data.shape[2:])
inputs[m] = m_data
# Record the kwargs required by `loss` and `predict`
loss_predict_kwargs = dict()
x = self.backbone(**inputs)
if stage == 'backbone':
return x, loss_predict_kwargs
if self.with_cls_head and stage == 'head':
x = self.cls_head(x, **loss_predict_kwargs)
return x, loss_predict_kwargs
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