U PhS@s*ddlmZddlmZddlmZddlZddlmZddl mm Z ddl m Z ddlmZddlmZmZddlmZd d d d gZGd ddejZGdddejZGdddejZGdddejZGdddejZGdddejZGdddejZGdd d ejZ e Z!Z"Z#dS)) annotations)Sequence)OptionalN) Convolution)UpSample)ConvPool)ensure_tuple_repBasicUnetPlusPlusBasicunetPlusPlusbasicunetplusplusBasicUNetPlusPlusKernelModifiedcs(eZdZdZfddZddZZS)Attention_blockz Attention Block c stt|ttj||dddddt||_ttj||dddddt||_ttj|ddddddtdt |_ tj dd|_ dS)NrT) kernel_sizestridepaddingbias)inplace) superr__init__nn SequentialConv3d BatchNorm3dW_gW_xSigmoidpsiReLUrelu)selfF_gF_lF_int __class__i/home/dell461/cl/sdc2/HISourceFinder-master-l/src/monai/networks/nets/basic_unetplusplus_modified_aspp.pyr(szAttention_block.__init__cCs4||}||}|||}||}||S)N)rrr r)r!gxg1x1rr'r'r(forward<s    zAttention_block.forward__name__ __module__ __qualname____doc__rr- __classcell__r'r'r%r(r#s rc@s eZdZdZdddddZdS) MCDropout3du>MC Dropout：无论 model.train()/eval() 都启用随机失活 torch.Tensor)inputreturncCstj||jd|jdS)NT)ptrainingr)F dropout3dr8r)r!r6r'r'r(r-GszMCDropout3d.forwardN)r/r0r1r2r-r'r'r'r(r4Dsr4c s:eZdZdZdddddddd d d d d fdd ZZS)TwoConvztwo convolutions.r?r?rrr?rint str | tuplebool float | tuple Sequence[int]Optional[tuple]zSequence[int] | int) spatial_dimsin_chnsout_chnsactnormrdropoutrrrc s\tt||||||||| | d } t||||||||| d } |d| |d| dS)a Args: spatial_dims: number of spatial dimensions. in_chns: number of input channels. out_chns: number of output channels. act: activation type and arguments. norm: feature normalization type and arguments. bias: whether to have a bias term in convolution blocks. dropout: dropout ratio. Defaults to no dropout. )rJrKrLrrrstrides)rJrKrLrrrconv_0conv_1N)rrr add_module) r!rGrHrIrJrKrrLrrrrNrOr%r'r(rNs4   zTwoConv.__init__)r=r>r@rr/r0r1r2rr3r'r'r%r(r<Ks  r<c s:eZdZdZdddddddd d d d d fdd ZZS)Downz-maxpooling downsampling and two convolutions.r=r>r@maxpoolrArBrCrDrErFstr) rGrHrIrJrKrrLrrdownsample_modec  s~t| dkr6td|fdd} |d| d} n| dkrDd} ntd| t||||||||| | d } |d | d S) a Args: spatial_dims: number of spatial dimensions. in_chns: number of input channels. out_chns: number of output channels. act: activation type and arguments. norm: feature normalization type and arguments. bias: whether to have a bias term in convolution blocks. dropout: dropout ratio. Defaults to no dropout. downsample_mode: 'maxpool' or 'strideconv'. Defaults to 'maxpool'. rSMAXrXrr max_poolingr strideconvzUnsupported downsample mode: )rrrconvsN)rrrrP ValueErrorr<)r!rGrHrIrJrKrrLrrrUrZ conv_strider\r%r'r(rs*   z Down.__init__)r=r>r@rSrQr'r'r%r(rRs  rRcseZdZfddZZS) _ASPPConvc s0ttj||d||ddt|tdS)Nr?F)rrdilationr)rrrrrr)r! in_channels out_channelsr`r%r'r(rsz_ASPPConv.__init__)r/r0r1rr3r'r'r%r(r_sr_cs$eZdZfddZddZZS)ASPPc stg}|ttj||dddt|tt|}|D]}|t |||qF|tt dtj||dddt|tt ||_ ttjt |j ||dddt|ttd|_dS)NrF)rg?)rrappendrrrrrtupler_AdaptiveAvgPool3d ModuleListr\lenDropoutproject)r!rarb atrous_ratesmodulesratesrater%r'r(rs6  z ASPP.__init__c CsNg}|jD]*}|tj|||jdddddq tj|dd}||S)NrX trilinearF)sizemode align_cornersrdim)r\rdr: interpolateshapetorchcatrj)r!r*resconvr'r'r(r-s  z ASPP.forwardr/r0r1rr-r3r'r'r%r(rcs !rccsXeZdZdZdd d d d d d d d ddddd d ddd dfdd ZdddddZZS)UpCatzHupsampling, concatenation with the encoder feature map, two convolutionsr=deconvdefaultlinearTr>r@FrArBrCrDrTznn.Module | str | Nonez bool | NonerErF)rGrHcat_chnsrIrJrKrrLupsamplepre_conv interp_moderrhalvesis_padrr attentionc st| dkr | dkr |}n| r,|dn|}t|||d| | | | |d |_t||||||||||d |_||_||_|jrt|||dd|_ dS)a Args: spatial_dims: number of spatial dimensions. in_chns: number of input channels to be upsampled. cat_chns: number of channels from the encoder. out_chns: number of output channels. act: activation type and arguments. norm: feature normalization type and arguments. bias: whether to have a bias term in convolution blocks. dropout: dropout ratio. Defaults to no dropout. upsample: upsampling mode, available options are ``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``. pre_conv: a conv block applied before upsampling. Only used in the "nontrainable" or "pixelshuffle" mode. interp_mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``} Only used in the "nontrainable" mode. align_corners: set the align_corners parameter for upsample. Defaults to True. Only used in the "nontrainable" mode. halves: whether to halve the number of channels during upsampling. This parameter does not work on ``nontrainable`` mode if ``pre_conv`` is `None`. is_pad: whether to pad upsampling features to fit features from encoder. Defaults to True. attention: whether to use attention gate. Defaults to False. nontrainableNrXrW)rqrrrrr)rr)r"r#r$) rrrrr<r\r use_attentionrattention_gate)r!rGrHrrIrJrKrrLrrrrrrrrrrup_chnsr%r'r(rsD,   zUpCat.__init__r5zOptional[torch.Tensor])r*x_ecCs||}|dk r|}|jr*|j||d}|jrt|jd}dg|d}t|D]4}|j| d|j| dkrTd||dd<qTtjj ||d}| tj ||gdd}n | |}|S)z Args: x: features to be upsampled. x_e: optional features from the encoder, if None, this branch is not in use. N)r)r*rXrr replicaters) rrrrrhrvrangerwr functionalpadr\rx)r!r*rx_0 x_e_non_none dimensionsspir'r'r(r-Hs"    z UpCat.forward) r=r}r~rTTTr>r@Fr.r'r'r%r(r|s 4Nr|c sneZdZddddddddd fd d difdd d d f dddddddddddd fdd ZddddZZS)r r?rrX) r@rF LeakyReLUg?T)negative_sloperinstanceaffiner=r}rArErCrBrDrTfloat) rGrarbfeaturesdeep_supervisionrJrKrrLr dropout_pc sTt||_| |_t|d} td| dt||| d|||| dd|_t|| d| d|||| dd|_ t|| d| d|||| dd|_ t|| d| d |||| dd d |_ t | d | d dd dgd|_ t|| d| d| d|||| | dddd |_t|| d| d| d|||| | dddd |_t|| d | d| d|||| | dddd |_t|| d | d | d |||| | dddd |_t|| d| dd| d|||| | dddd |_t|| d| dd| d|||| | dddd |_t|| d | dd| d|||| | dddd |_t|| d| dd | d|||| | dddd |_t|| d| dd | d|||| | dddd |_t|| d| dd | d|||| | dddd |_|jr|jdkrt|jnt|_td|f| d|dd|_td|f| d|dd|_td|f| d|dd|_ td|f| d|dd|_!dS)a 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 `_, 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` zBasicUNetPlusPlus features: .r)r?r?rYrrXr?r[)rrU )rarbrkFT)rrrrzN)"rrrrr printr<conv_0_0rRconv_1_0conv_2_0conv_3_0rcasppr| 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_4r4rIdentitymc_dropout_outrfinal_conv_0_1final_conv_0_2final_conv_0_3final_conv_0_4) r!rGrarbrrrJrKrrLrrfear%r'r(rgsA                z(BasicUNetPlusPlusKernelModified.__init__r5)r*cCs$||}||}|||}||}|||}||tj||gdd}||}| ||} | | tj||gdd} | | tj|||gdd} | |} | | |} || tj|| gdd}||tj||| gdd}||tj|||| gdd}||}||}|g}|S)a Args: x: input should have spatially N dimensions ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N-1])``, N is defined by `dimensions`. It is recommended to have ``dim_n % 16 == 0`` to ensure all maxpooling inputs have even edge lengths. Returns: A torch Tensor of "raw" predictions in shape ``(Batch, out_channels, dim_0[, dim_1, ..., dim_N-1])``. rrs)rrrrrrrwrxrrrrrrrrrrr)r!r*x_0_0x_1_0x_0_1x_2_0x_1_1x_0_2x_3_0x_2_1x_1_2x_0_3x_4_0x_3_1x_2_2x_1_3x_0_4 output_0_4outputr'r'r(r-}s&           z'BasicUNetPlusPlusKernelModified.forwardr{r'r'r%r(r fs  ()$ __future__rcollections.abcrtypingrrwtorch.nnrtorch.nn.functionalrr:"monai.networks.blocks.convolutionsrmonai.networks.blocks.upsamplermonai.networks.layers.factoriesrrmonai.utils.miscr __all__Moduler Dropout3dr4rr<rRr_rcr|r r r r r'r'r'r( s4       !86.oG