o s·¯iiã@s:ddlZddlmZddlmZmZGdd„dejƒZdS)éNé)Ú activationsÚnormscs,eZdZdZd ‡fdd„ Zd dd „Z‡ZS) ÚTACa_Transform-Average-Concatenate inter-microphone-channel permutation invariant communication block [1]. Args: input_dim (int): Number of features of input representation. hidden_dim (int, optional): size of hidden layers in TAC operations. activation (str, optional): type of activation used. See asteroid.masknn.activations. norm_type (str, optional): type of normalization layer used. See asteroid.masknn.norms. .. note:: Supports inputs of shape :math:`(batch, mic\_channels, features, chunk\_size, n\_chunks)` as in FasNet-TAC. The operations are applied for each element in ``chunk_size`` and ``n_chunks``. Output is of same shape as input. References [1] : Luo, Yi, et al. "End-to-end microphone permutation and number invariant multi-channel speech separation." ICASSP 2020. é€ÚpreluÚgLNcs‚tƒ ¡||_t t ||¡t |¡ƒ¡|_t t ||¡t |¡ƒ¡|_ t t d||¡t |¡ƒ¡|_ t  |¡|ƒ|_ dS)Né) ÚsuperÚ__init__Ú hidden_dimÚnnÚ SequentialÚLinearrÚgetÚinput_tfÚavg_tfÚ concat_tfrÚnorm)ÚselfÚ input_dimr Ú activationÚ norm_type©Ú __class__©úG/home/ubuntu/.local/lib/python3.10/site-packages/asteroid/masknn/tac.pyr s ÿÿÿz TAC.__init__Nc sZ| ¡\}}}}}ˆdurt |g|¡‰| | ddddd¡ |||||¡¡ |||||j¡‰ˆ ¡dkr?ˆ d¡}n‡‡fdd„t |ƒDƒ}t  |d¡}|  | ||||j¡¡}| ||||j¡  d¡  ˆ¡}t  ˆ|gd ¡‰| ˆ ||||d ¡¡ ||||d ¡‰| ˆ ddddd¡ ||d ||¡¡ ||d ||¡‰ˆ|7‰ˆS) aB Args: x: (:class:`torch.Tensor`): Input multi-channel DPRNN features. Shape: :math:`(batch, mic\_channels, features, chunk\_size, n\_chunks)`. valid_mics: (:class:`torch.LongTensor`): tensor containing effective number of microphones on each batch. Batches can be composed of examples coming from arrays with a different number of microphones and thus the ``mic_channels`` dimension is padded. E.g. torch.tensor([4, 3]) means first example has 4 channels and the second 3. Shape: :math`(batch)`. Returns: output (:class:`torch.Tensor`): features for each mic_channel after TAC inter-channel processing. Shape :math:`(batch, mic\_channels, features, chunk\_size, n\_chunks)`. Nréérr c s8g|]}ˆ|dd…dd…dˆ|…f d¡ d¡‘qS)Nr r)ÚmeanÚ unsqueeze)Ú.0Úb©ÚoutputÚ valid_micsrrÚ Ds,ÿzTAC.forward..éÿÿÿÿ)ÚsizeÚtorchÚ LongTensorrÚpermuteÚreshaper ÚmaxrÚrangeÚcatrr Ú expand_asrr) rÚxr%Ú batch_sizeÚnmicsÚchannelsÚ chunk_sizeÚn_chunksÚ mics_meanrr#rÚforward&sD$ÿþ   ÿ ÿýÿþ ÿþz TAC.forward)rrr)N)Ú__name__Ú __module__Ú __qualname__Ú__doc__r r8Ú __classcell__rrrrrsr)r)Útorch.nnr ÚrrÚModulerrrrrÚs