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    )annotations)SequenceN)Conv)DownTwoConvUpCat)ensure_tuple_rep)BasicUnetPlusPlusBasicunetPlusPlusbasicunetplusplusBasicUNetPlusPlusc                
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        A UNet++ implementation with 1D/2D/3D supports.

        Based on:

            Zhou et al. "UNet++: A Nested U-Net Architecture for Medical Image
            Segmentation". 4th Deep Learning in Medical Image Analysis (DLMIA)
            Workshop, DOI: https://doi.org/10.48550/arXiv.1807.10165


        Args:
            spatial_dims: number of spatial dimensions. Defaults to 3 for spatial 3D inputs.
            in_channels: number of input channels. Defaults to 1.
            out_channels: number of output channels. Defaults to 2.
            features: six integers as numbers of features.
                Defaults to ``(32, 32, 64, 128, 256, 32)``,

                - the first five values correspond to the five-level encoder feature sizes.
                - the last value corresponds to the feature size after the last upsampling.

            deep_supervision: whether to prune the network at inference time. Defaults to False. If true, returns a list,
                whose elements correspond to outputs at different nodes.
            act: activation type and arguments. Defaults to LeakyReLU.
            norm: feature normalization type and arguments. Defaults to instance norm.
            bias: whether to have a bias term in convolution blocks. Defaults to True.
                According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,
                if a conv layer is directly followed by a batch norm layer, bias should be False.
            dropout: dropout ratio. Defaults to no dropout.
            upsample: upsampling mode, available options are
                ``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.

        Examples::

            # for spatial 2D
            >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128))

            # for spatial 2D, with deep supervision enabled
            >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), deep_supervision=True)

            # for spatial 2D, with group norm
            >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), norm=("group", {"num_groups": 4}))

            # for spatial 3D
            >>> net = BasicUNetPlusPlus(spatial_dims=3, features=(32, 32, 64, 128, 256, 32))

        See Also
            - :py:class:`monai.networks.nets.BasicUNet`
            - :py:class:`monai.networks.nets.DynUNet`
            - :py:class:`monai.networks.nets.UNet`

           zBasicUNetPlusPlus features: .r   r   r   r      F)halves   conv)kernel_sizeN)super__init__r    r   printr   conv_0_0r   conv_1_0conv_2_0conv_3_0conv_4_0r   	upcat_0_1	upcat_1_1	upcat_2_1	upcat_3_1	upcat_0_2	upcat_1_2	upcat_2_2	upcat_0_3	upcat_1_3	upcat_0_4r   final_conv_0_1final_conv_0_2final_conv_0_3final_conv_0_4)selfr   r   r   r   r    r"   r$   r%   r&   r(   fea	__class__ h/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/networks/nets/basic_unetplusplus.pyr2      s:   
@
*&&&$**$*$ zBasicUNetPlusPlus.__init__xtorch.Tensorc                 C  sN  |  |}| |}| ||}| |}| ||}| |tj||gdd}| |}| 	||}	| 
|	tj||gdd}
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tj|||gdd}| |}| ||}| |tj||	gdd}| |tj|||
gdd}| |tj||||gdd}| |}| |}| |}| |}| jr||||g}|S |g}|S )a  
        Args:
            x: input should have spatially N dimensions
                ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N-1])``, N is defined by `dimensions`.
                It is recommended to have ``dim_n % 16 == 0`` to ensure all maxpooling inputs have
                even edge lengths.

        Returns:
            A torch Tensor of "raw" predictions in shape
            ``(Batch, out_channels, dim_0[, dim_1, ..., dim_N-1])``.
        r   )dim)r4   r5   r9   r6   r:   r=   torchcatr7   r;   r>   r@   r8   r<   r?   rA   rB   rC   rD   rE   rF   r    )rG   rM   Zx_0_0Zx_1_0Zx_0_1Zx_2_0Zx_1_1Zx_0_2Zx_3_0Zx_2_1Zx_1_2Zx_0_3Zx_4_0Zx_3_1Zx_2_2Zx_1_3Zx_0_4Z
output_0_1Z
output_0_2Z
output_0_3Z
output_0_4outputrK   rK   rL   forward   s0   

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zBasicUNetPlusPlus.forward)r   r   r   r   r   r   r   r   r    r!   r"   r#   r$   r#   r%   r!   r&   r'   r(   r)   )rM   rN   )__name__
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fr   )
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