a h5@sddlZddlmZddlmZmZmZddlZddlmZddl m Z m Z ej j jZGdddej jZGd d d ej jZGd d d eZGd ddeZGdddej jZGdddej jZGdddej jZdS)N)Sequence)CallableOptionalUnion)Tensor)_log_api_usage_once _make_ntuplecsreZdZdZdeedfdd Zeeee e ee ee edfdd Z e e d d d Z ed d dZZS)FrozenBatchNorm2da! BatchNorm2d where the batch statistics and the affine parameters are fixed Args: num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)`` eps (float): a value added to the denominator for numerical stability. Default: 1e-5 h㈵>) num_featuresepscsdtt|||_|dt||dt||dt||dt|dS)Nweightbias running_mean running_var)super__init__rr Zregister_buffertorchZoneszeros)selfr r  __class__B/var/www/auris/lib/python3.9/site-packages/torchvision/ops/misc.pyrs zFrozenBatchNorm2d.__init__) state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsc s2|d}||vr||=t|||||||dS)NZnum_batches_tracked)r_load_from_state_dict) rrrrrrr r!Znum_batches_tracked_keyrrrr"$s z'FrozenBatchNorm2d._load_from_state_dictxreturncCsr|jdddd}|jdddd}|jdddd}|jdddd}|||j}|||}|||S)N)rZreshaperrrr Zrsqrt)rr$wbrvZrmscalerrrrforward6s zFrozenBatchNorm2d.forward)r%cCs$|jjd|jjdd|jdS)N(rz, eps=))r__name__rshaper )rrrr__repr__AszFrozenBatchNorm2d.__repr__)r )r/ __module__ __qualname____doc__intfloatrdictstrboollistr"rr,r1 __classcell__rrrrr s   r cseZdZddddejjejjdddejjf eee ee edffe ee edffe e ee edfe fee e dejjfe e dejjfe ee edffe ee ee dejjfdd fdd ZZS) ConvNormActivationr&NT.) in_channels out_channels kernel_sizestridepaddinggroups norm_layeractivation_layerdilationinplacer conv_layerr%c  s|durxttr.ttr.dd}nJttr@tnt} t| t| tfddt| D}| dur|du} | |||||| dg}|dur||||dur| durind| i}||fi|tj |t |||_ |j t krtddS)Nr&rc3s&|]}|dd|VqdS)r&rNr).0irFr@rr ]z.ConvNormActivation.__init__..)rFrCrrGzhDon't use ConvNormActivation directly, please use Conv2dNormActivation and Conv3dNormActivation instead.) isinstancer5rlenr tuplerangeappendrrrr?rr<warningswarn)rr>r?r@rArBrCrDrErFrGrrHZ _conv_dimlayersparamsrrKrrFs@     zConvNormActivation.__init__)r/r2r3rnn BatchNorm2dReLUConv2dr5rrPrr8rModuler9rr;rrrrr<Es2r<cseZdZdZddddejjejjdddf eee ee eeffe ee eeffe e ee eefe fee e dejjfe e dejjfe ee eeffe ee edd fdd ZZS) Conv2dNormActivationa Configurable block used for Convolution2d-Normalization-Activation blocks. Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block kernel_size: (int, optional): Size of the convolving kernel. Default: 3 stride (int, optional): Stride of the convolution. Default: 1 padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will be calculated as ``padding = (kernel_size - 1) // 2 * dilation`` groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer won't be used. Default: ``torch.nn.BatchNorm2d`` activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU`` dilation (int): Spacing between kernel elements. Default: 1 inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True`` bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``. r=r&NT. r>r?r@rArBrCrDrErFrGrr%c s*t||||||||| | | tjj dSN)rrrrWrZ rr>r?r@rArBrCrDrErFrGrrrrrszConv2dNormActivation.__init__)r/r2r3r4rrWrXrYr5rrPrr8rr[r9rr;rrrrr\~s0r\cseZdZdZddddejjejjdddf eee ee eeeffe ee eeeffe e ee eeefe fee e dejjfe e dejjfe ee eeeffe ee edd fdd ZZS) Conv3dNormActivationa Configurable block used for Convolution3d-Normalization-Activation blocks. Args: in_channels (int): Number of channels in the input video. out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block kernel_size: (int, optional): Size of the convolving kernel. Default: 3 stride (int, optional): Stride of the convolution. Default: 1 padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will be calculated as ``padding = (kernel_size - 1) // 2 * dilation`` groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer won't be used. Default: ``torch.nn.BatchNorm3d`` activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU`` dilation (int): Spacing between kernel elements. Default: 1 inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True`` bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``. r=r&NT.r]c s*t||||||||| | | tjj dSr^)rrrrWZConv3dr_rrrrszConv3dNormActivation.__init__)r/r2r3r4rrWZ BatchNorm3drYr5rrPrr8rr[r9rr;rrrrr`s0r`csteZdZdZejjejjfeee dejj fe dejj fddfdd Z e e ddd Z e e dd d ZZS) SqueezeExcitationaE This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1). Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3. Args: input_channels (int): Number of channels in the input image squeeze_channels (int): Number of squeeze channels activation (Callable[..., torch.nn.Module], optional): ``delta`` activation. Default: ``torch.nn.ReLU`` scale_activation (Callable[..., torch.nn.Module]): ``sigma`` activation. Default: ``torch.nn.Sigmoid`` .N)input_channelssqueeze_channels activationscale_activationr%csXtt|tjd|_tj||d|_tj||d|_ ||_ ||_ dS)Nr&) rrrrrWZAdaptiveAvgPool2davgpoolrZfc1fc2rdre)rrbrcrdrerrrrs zSqueezeExcitation.__init__)inputr%cCs2||}||}||}||}||Sr^)rfrgrdrhrerrir+rrr_scales     zSqueezeExcitation._scalecCs||}||Sr^)rkrjrrrr,s zSqueezeExcitation.forward)r/r2r3r4rrWrYZSigmoidr5rr[rrrkr,r;rrrrrasrac sjeZdZdZdejjdddfeeee e dejj fe e dejj fe e e e dfdd ZZS) MLPaThis block implements the multi-layer perceptron (MLP) module. Args: in_channels (int): Number of channels of the input hidden_channels (List[int]): List of the hidden channel dimensions norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the linear layer. If ``None`` this layer won't be used. Default: ``None`` activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the linear layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU`` inplace (bool, optional): Parameter for the activation layer, which can optionally do the operation in-place. Default is ``None``, which uses the respective default values of the ``activation_layer`` and Dropout layer. bias (bool): Whether to use bias in the linear layer. Default ``True`` dropout (float): The probability for the dropout layer. Default: 0.0 NTg.)r>hidden_channelsrDrErGrdropoutc s|dur ind|i}g} |} |ddD]d} | tjj| | |d|durZ| || | |fi|| tjj|fi|| } q(| tjj| |d|d| tjj|fi|tj| t|dS)NrGr')r)rRrrWZLinearZDropoutrrr) rr>rmrDrErGrrnrVrUZin_dimZ hidden_dimrrrrs  z MLP.__init__)r/r2r3r4rrWrYr5r:rrr[r9r6rr;rrrrrlsrlcs:eZdZdZeedfdd ZeedddZZ S)PermutezThis module returns a view of the tensor input with its dimensions permuted. Args: dims (List[int]): The desired ordering of dimensions )dimscst||_dSr^)rrrp)rrprrrr<s zPermute.__init__r#cCst||jSr^)rZpermuterp)rr$rrrr,@szPermute.forward) r/r2r3r4r:r5rrr,r;rrrrro5sro)rScollections.abcrtypingrrrrrutilsrr rWZ functionalZ interpolater[r Z Sequentialr<r\r`rarlrorrrrs   7921'-