o si@sLddlZddlZddlmZddZGdddejjZGdd d eZ dS) N) complex_nncGstjjdd|DS)NcSsg|]}|dur|qS)N).0xrrH/home/ubuntu/.local/lib/python3.10/site-packages/asteroid/masknn/base.py sz$_none_sequential..)torchnn Sequential)argsrrr_none_sequentialsr cs.eZdZdZddfdd ZddZZS)BaseUNetaBase class for u-nets with skip connections between encoders and decoders. (For u-nets without skip connections, simply use a `nn.Sequential`.) Args: encoders (List[torch.nn.Module] of length `N`): List of encoders decoders (List[torch.nn.Module] of length `N - 1`): List of decoders output_layer (Optional[torch.nn.Module], optional): Layer after last decoder. N) output_layercsRt|t|dks Jttj||_tj||_|p%tj|_ dSN) lensuper__init__r r ModuleListencodersdecodersIdentityr)selfrrr __class__rrrs  zBaseUNet.__init__cCsxg}t|jD] \}}||}||qttt|dd|jD]\}\}}||}tj||gdd}q#||S)Nr)dim) enumeraterappendzipreversedrr catr)rrenc_outsidxencenc_outdecrrrforward&s ( zBaseUNet.forward)__name__ __module__ __qualname____doc__rr( __classcell__rrrrr s rcsVeZdZdZeZeddefddZdfdd Z fd d Z d d Z ddZ Z S)BaseDCUMaskNeta Base class for DCU-style mask nets. Used for DCUMaskNet and DCCRMaskNet. The preferred way to instantiate this class is to use the ``default_architecture()`` classmethod. Args: encoders (List[torch.nn.Module]): List of encoders decoders (List[torch.nn.Module]): List of decoders output_layer (Optional[torch.nn.Module], optional): Layer after last decoder, before mask application. mask_bound (Optional[str], optional): Type of mask bound to use, as defined in [1]. Valid values are "tanh" ("BDT mask"), "sigmoid" ("BDSS mask"), None (unbounded mask). References - [1] : "Phase-aware Speech Enhancement with Deep Complex U-Net", Hyeong-Seok Choi et al. https://arxiv.org/abs/1903.03107 r architecturec KsN|j|\}}|d^}}}g|dd||g|RR}|||fi|S)a`Create a masknet instance from a predefined, named architecture. Args: architecture (str): Name of predefined architecture. Valid values are dependent on the concrete subclass of ``BaseDCUMaskNet``. n_src (int, optional): Number of sources kwargs (optional): Passed to ``__init__``. rN)_architectures) clsr/n_srckwargsrrin_chan_ignored_out_chanrestrrrdefault_architectureFs  z#BaseDCUMaskNet.default_architectureNtanhc s0||_tjd||t|t|d|dS)N)rrrr) mask_boundrrr rBoundComplexMask)rrrrr9r3rrrrVs zBaseDCUMaskNet.__init__cs,||}t|d}|||}|Sr)fix_input_dimsrr( unsqueezefix_output_dims)rrfixed_xoutrrrr(bs  zBaseDCUMaskNet.forwardcC|S)zAOverwrite this in subclasses to implement input dimension checks.r)rrrrrr;hszBaseDCUMaskNet.fix_input_dimscCr@)zOverwrite this in subclasses to implement output dimension checks. y is the output and x was the input (passed to use the shape).r)ryrrrrr=lszBaseDCUMaskNet.fix_output_dims)r)Nr8)r)r*r+r,NotImplementedr0 classmethodstrr7rr(r;r=r-rrrrr.1s r.) numpynpr rr r Modulerr.rrrrs  &