|jD:UddlmZddlZddlmZddlmZddlZddlmZddl m Z ddl m Z m Z mZmZmZmZmZddlmZdd lmZer0dd lmZmZdd lmZdd lmZdd lmZedZGddeZgZddddddZ de!d<Gddej"Z#Gddej"Z$Gddej"Z%Gddej"Z&Gd d!ej"Z'Gd"d#ej"Z(d4d-Z) d5d6d/Z* d5d6d0Z+ d5d6d1Z, d5d6d2Z- d5d6d3Z.dS)7) annotationsN) TYPE_CHECKING)Unpack)nn) ParamAttr)AdaptiveAvgPool2D AvgPool2D BatchNormConv2DDropoutLinear MaxPool2D)Uniform)get_weights_path_from_url)Literal TypedDict) NotRequired)Tensor)Size2) densenet121 densenet161 densenet169 densenet201 densenet264c8eZdZUded<ded<ded<ded<dS) _DenseNetOptionszNotRequired[int]bn_sizezNotRequired[float]dropout num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____annotations__m/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/vision/models/densenet.pyrr4sB!!!!####%%%%$$$$$$r&r)zYhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams db1b239ed80a905290fd8b01d3af08e4)zYhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams 62158869cb315098bd25ddbfd308a853)zYhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams 82cc7c635c3f19098c748850efb2d796)zYhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams 16ca29565a7712329cf9e36e02caaf58)zYhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams 3270ce516b85370bba88cfdd9f60bff4zdict[str, tuple[str, str]] model_urlsc2eZdZ ddfd ZddZxZS) BNACConvLayerrrelu num_channelsint num_filters filter_sizerstridepadgroupsactstrreturnNonec tt|||_t ||||||t d|_dS)Nr9F in_channels out_channels kernel_sizer6paddingr8 weight_attr bias_attr)super__init__r _batch_normr r_conv selfr2r4r5r6r7r8r9 __class__s r'rGzBNACConvLayer.__init__Vsf $\s;;;$$#!      r&inputrcZ||}||}|SN)rHrIrKrMys r'forwardzBNACConvLayer.forwardns)   U # # JJqMMr&r0rr0r1r2r3r4r3r5rr6rr7rr8r3r9r:r;r<rMrr;rr!r"r#rGrR __classcell__rLs@r'r/r/Usd        0r&r/c4eZdZUded<d fd Zdd ZxZS) DenseLayerfloatrr2r3 growth_raterr;r<ct||_t|||zddd|_t||z|ddd|_|rt |d|_dSdS)Nr0rr2r4r5r7r6downscale_in_infer)pmode)rFrGrr/ bn_ac_func1 bn_ac_func2r dropout_func)rKr2r\rrrLs r'rGzDenseLayer.__init__ws  (%+-    ) ;.#      N ''8L M M MD    N Nr&rMrc||}||}|jr||}t j||gd}|S)Nr0)axis)rcrdrrepaddleconcat)rKrMconvs r'rRzDenseLayer.forwardsb&&%% < +$$T**D}eT]333 r&) r2r3r\r3rr3rr[r;r<rUr!r"r#r$rGrRrWrXs@r'rZrZtsbNNNNNNNNN2r&rZc8eZdZUded< ddfd ZddZxZS) DenseBlockr[rNr2r3 num_layersrr\name str | Noner;r<c $t||_g|_|}t |D]N}|j||d|dzt||||||z}OdS)N_r0)r2r\rr)rFrGrdense_layer_funcrangeappend add_sublayerrZ) rKr2rnrr\rro pre_channellayerrLs r'rGzDenseBlock.__init__s  "" :&& 4 4E  ! ( (!!))eai))%0$/ ' '    & 3KK 4 4r&rMrc4|}|jD] }||}|SrO)rs)rKrMrjfuncs r'rRzDenseBlock.forwards-)  D4::DD r&rO)r2r3rnr3rr3r\r3rr[rorpr;r<rUrkrXs@r'rmrmsfNNN 44444448r&rmc(eZdZd fd Zd d ZxZS) TransitionLayerr2r3num_output_featuresr;r<ctt||ddd|_t ddd|_dS)Nr0rr^rBr6rC)rFrGr/ conv_ac_funcr pool2d_avg)rKr2r}rLs r'rGzTransitionLayer.__init__s[ )%+    $!QGGGr&rMrcZ||}||}|SrO)rrrPs r'rRzTransitionLayer.forwards+   e $ $ OOA  r&)r2r3r}r3r;r<rUrVrXs@r'r|r|sW H H H H H Hr&r|c2eZdZ ddfd ZddZxZS) ConvBNLayerr0rr1r2r3r4r5rr6r7r8r9r:r;r<c tt||||||td|_t |||_dS)NFr?r>)rFrGr rrIr rHrJs r'rGzConvBNLayer.__init__sj $$#!      %[c:::r&rMrcZ||}||}|SrO)rIrHrPs r'rRzConvBNLayer.forwards+ JJu     Q  r&rSrTrUrVrXs@r'rrsd ;;;;;;;0r&rcNeZdZUdZded<ded< ddfd ZddZxZS)DenseNeta DenseNet model from `"Densely Connected Convolutional Networks" `_. Args: layers (int, optional): Layers of DenseNet. Default: 121. bn_size (int, optional): Expansion of growth rate in the middle layer. Default: 4. dropout (float, optional): Dropout rate. Default: :math:`0.0`. num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of DenseNet model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import DenseNet >>> # Build model >>> densenet = DenseNet() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = densenet(x) >>> print(out.shape) paddle.Size([1, 1000]) r3rboolr yTlayersrrr[r;r<ctt||_||_gd}||vsJd|d|ddgdfddgd fddgd fddgd fddgd fd}||\}} } t d |ddd d|_t d dd|_| |_g|_ g|_ |} |} t| D]\} }|j | d| dzt| ||| |dt| dzz| || zz} | } | t!| dz krQ|j | d| dzdt#| | dz| dz} | dz} t%| d|_|jrt)d|_|jdkr]dt-j| dzz }t1| |t3t5| |t3|_dSdS)N)rzsupported layers are z but input layer is @ ) `0)rr$r)rrrr)rrrr)rrrrr_rr1)r2r4r5r6r7r9r0rdb_conv_rj)r2rnrr\rrotr_conv_blk)r2r}r>rg?) initializer)rDrE)rFrGrr r conv1_funcr pool2d_max block_configdense_block_func_listtransition_func_list enumeraterurvrmr:lenr|r batch_normrrmathsqrtr rrout)rKrrrrr supported_layers densenet_specnum_init_featuresr\rpre_num_channels num_featuresirnstdvrLs r'rGzDenseNet.__init__s &"444)))) R$4 R R& R R*))b///*b///*b///*b///*b///*   8EV7L4; %)    $!QGGG(%'"$&!,( &|44 1 1MAz  & - -!!&q1u&&%5#- '$/ '#c!a%jj0      (*{*BBL+ C %%))))00%%-!a%---')90<0A$01#4 +q0 #Lf=== > 3/22DO  a  <##5666D%'4%2F2FGGG#++ DHHH r&rMrc||}||}t|jD]L\}}|j||}|t |jdz kr|j||}M||}|jr| |}|j dkr,tj |dd}| |}|S)Nr0r) start_axis stop_axis)rrrrrrrrr rrrhflattenr)rKrMrjrrnrQs r'rRzDenseNet.forwardfsu%%t$$&t'899 : :MAz04-a066DC)**Q...3t03D99t$$ > &%%A  a  qQ"===A Ar&)rrrrT) rr3rr3rr[rr3r rr;r<rU)r!r"r#__doc__r$rGrRrWrXs@r'rrs<OOO PPPPPPPdr&rarch _DenseNetArchrr3 pretrainedrkwargsUnpack[_DenseNetOptions]r;c tdd|i|}|rq|tvs J|dtt|dt|d}tj|}|||S)NrzJ model do not have a pretrained model now, you should set pretrained=Falserr0r%)rr-rrhloadset_dict)rrrrmodel weight_pathparams r' _densenetr{s  - -F -f - -E z!!! _ _ _"!!0 t Q D!1!!4   K(( u Lr&Fc  tdd|fi|S)aDenseNet 121-layer model from `"Densely Connected Convolutional Networks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of DenseNet 121-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import densenet121 >>> # Build model >>> model = densenet121() >>> # Build model and load imagenet pretrained weight >>> # model = densenet121(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrrrrs r'rr@ ]C > >v > >>r&c  tdd|fi|S)aDenseNet 161-layer model from `"Densely Connected Convolutional Networks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of DenseNet 161-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import densenet161 >>> # Build model >>> model = densenet161() >>> # Build model and load imagenet pretrained weight >>> # model = densenet161(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrrrs r'rrrr&c  tdd|fi|S)aDenseNet 169-layer model from `"Densely Connected Convolutional Networks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of DenseNet 169-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import densenet169 >>> # Build model >>> model = densenet169() >>> # Build model and load imagenet pretrained weight >>> # model = densenet169(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrrrs r'rrrr&c  tdd|fi|S)aDenseNet 201-layer model from `"Densely Connected Convolutional Networks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of DenseNet 201-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import densenet201 >>> # Build model >>> model = densenet201() >>> # Build model and load imagenet pretrained weight >>> # model = densenet201(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrrrs r'rrs> ]C > >v > >>r&c  tdd|fi|S)aDenseNet 264-layer model from `"Densely Connected Convolutional Networks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of DenseNet 264-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import densenet264 >>> # Build model >>> model = densenet264() >>> # Build model and load imagenet pretrained weight >>> # model = densenet264(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrrrs r'rrrr&) rrrr3rrrrr;r)F)rrrrr;r)/ __future__rrtypingrtyping_extensionsrrhrpaddle.base.param_attrr paddle.nnrr r r r r rpaddle.nn.initializerrpaddle.utils.downloadrrrrrpaddle._typingrrr__all__r-r$Layerr/rZrmr|rrrrrrrrr%r&r'rs#"""""" $$$$$$ ,,,,,,*)))));;;;;;%))))))))------$$$$$$ M%%%%%9%%% #** 0BH>""""""""J########Lbh("(>FFFFFrxFFFR, ? ? ? ? ?H ? ? ? ? ?H ? ? ? ? ?H?????F ? ? ? ? ? ? ?r&