ёi(%SSKJr SSKJrJr SSKJrJr SSKrSSK J s J r SSKJ r SSK Jr SSKJrJrJrJrJrJr SSKJr SS KJr \(aSS KJr SS KJr "S S \5r/rSS0rSSjr "SS\ RB5r""SS\ RB5r#"SS\ RB5r$SSSjjr%g)) annotations) TYPE_CHECKING TypedDict) NotRequiredUnpackN)nn) ParamAttr)AdaptiveAvgPool2D AvgPool2DConv2DDropoutLinear MaxPool2D)Uniform)get_weights_path_from_url)Tensor)Size2c*\rSrSr%S\S'S\S'Srg)_GoogLeNetOptions+zNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____firstlineno____annotations____static_attributes__r^/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/vision/models/googlenet.pyrr+s%%$$r r googlenet)zWhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams 80c06f038e905c53ab32c40eca6e26aecJSUS-U-- S-n[[U*U5S9nU$)Ng@g?) initializer)r r)channels filter_sizestdv param_attrs r!xavierr+:s3 ;>H, -# 5Dwud';[TU]5 [UUUUUS- S-USS9Ulg)Nr%F) in_channels out_channels kernel_sizestridepaddinggroups bias_attr)super__init__r _conv)self num_channels num_filtersr(r4r6 __class__s r!r9ConvLayer.__init__As: $$# 1_*  r c(URU5nU$)Nr:)r;inputsys r!forwardConvLayer.forwardUs JJv r rA)r0r0) r<intr=rFr(rFr4rr6rFrBrreturnrrrrrr9rDr __classcell__r>s@r!r-r-@sO            (r r-cV^\rSrSrSU4SjjrSSjrSrU=r$) InceptionZc >[T U]5 [XS5Ul[XS5Ul[XES5Ul[XS5Ul[XgS5Ul[SSSS9Ul [XS5Ul g)Nr0)r3r4r5) r8r9r-_conv1_conv3r_conv3_conv5r_conv5r_pool_convprj) r;input_channelsoutput_channelsfilter1filter3Rfilter3filter5Rfilter5projr>s r!r9Inception.__init__[st ;  1= 15  1= 15 1QB !.: r cNURU5nURU5nURU5nURU5nUR U5nUR U5nUR U5n[R"X$Xh/SS9n [R"U 5n U $)Nr0axis) rRrSrTrUrVrWrXpaddleconcatFrelu) r;rBconv1conv3rconv3conv5rconv5poolconvprjcats r!rDInception.forwardqs F#f% F#f% F#zz&!--%mmU5:CffSk r )rRrTrSrVrUrXrW)rYrFrZrFr[rFr\rFr]rFr^rFr_rFr`rFrGrIrKs@r!rMrMZs`;;; ;  ;  ;;;;,r rMcT^\rSrSr%SrS\S'S\S'S S U4SjjjrS SjrS rU=r $) GoogLeNetaNGoogLeNet (Inception v1) model architecture from `"Going Deeper with Convolutions" `_. Args: num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import GoogLeNet >>> # Build model >>> model = GoogLeNet() >>> x = paddle.rand([1, 3, 224, 224]) >>> out, out1, out2 = model(x) >>> print(out.shape, out1.shape, out2.shape) paddle.Size([1, 1000]) paddle.Size([1, 1000]) paddle.Size([1, 1000]) rFrboolrc >[TU]5 XlX l[ SSSS5Ul[ SSS9Ul[ SSS5Ul[ SSS5Ul [SSSSS S S S 5Ul [S S S S SS SS5Ul [S S SSSS SS5Ul [SSSSSSSS5Ul[SSS S S SSS5Ul[SSSSSS SS5Ul[SSS SSS S S 5Ul[SSS SSS S S 5Ul[SSSSSSS S 5UlU(a.[)S5Ul[-SSS9Ul[-SSS9UlUS:a[3SSS9Ul[7S U[9S S5S!9Ul[ SS S5Ul[7S"S [9S#S5S!9Ul[3S$SS9Ul [7S U[9S S5S!9Ul![ SS S5Ul"[7S"S [9S#S5S!9Ul#[3S$SS9Ul$[7S U[9S S5S!9Ul%gg)%NrPr.r%)r3r4r0` i0ipi ii@i@irQrg?downscale_in_infer)pmodei) weight_attriigffffff?)&r8r9rrr-r:rrW_conv_1_conv_2rM_ince3a_ince3b_ince4a_ince4b_ince4c_ince4d_ince4e_ince5a_ince5br _pool_5r _pool_o1_pool_o2r _droprr+_fc_out_conv_o1_fc_o1_drop_o1_out1_conv_o2_fc_o2_drop_o2_out2)r;rrr>s r!r9GoogLeNet.__init__s< &"q"a+ 1Q7  R+  S!,  c2r3BC  c3S"b"E  c3CRD  c3S"b"E  c3S"b"E  c3S"b"E  c3S"c3G  c3S"c3G  c3S"c3G ,Q/DL%!A>DM%!A>DM ? 3-ABDJ!kvdADL &c32DM taIDK#c0DEDMkvdAODJ&c32DM taIDK#c0DEDMkvdAODJ# r cURU5nURU5nURU5nURU5nURU5nUR U5nUR U5nURU5nUR U5nURU5nURU5nURU5nURU5nURU5nURU5nURU5nXSUpnUR(a3URU5nURU5nUR!U5nUR"S:GaUR%U5n[&R("USS/S9nUR+U5nUR-U5n[&R."USSS9nUR1U5n[2R4"U5nUR7U5nUR9U5nUR;U5n[&R."USSS9nUR=U5nUR?U5nURAU5nXgU4$)Nrr%rPrcr0) start_axis stop_axis)!r:rWrrrrrrrrrrrrrrrrrresqueezerrflattenrrgrhrrrrrr) r;rBxince4aince4dince5boutout1out2s r!rDGoogLeNet.forwards JJv  JJqM LLO LLO JJqM LLO LLO JJqMa LL  LLOa LL  JJqM LLOa &4 >>,,s#C==&D==&D   a **S/C..Aq62C,,s#C==&D>>$1CD;;t$D66$>$1CD;;t$D==&D::d#D$r )r:rrrrrrrrrrrrrrrrrrrrrrWrrrrr)iT)rrFrrurHNone)rBrrHztuple[Tensor, Tensor, Tensor]) rrrr__doc__rr9rDrrJrKs@r!rsrss,6O/P/Pb..r rsc [S0UD6nSnU(a[U[;d US35e[[US[US5n[R"U5nUR U5 U$)aGoogLeNet (Inception v1) model architecture from `"Going Deeper with Convolutions" `_. 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:`GoogLeNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import googlenet >>> # Build model >>> model = googlenet() >>> # Build model and load imagenet pretrained weight >>> # model = googlenet(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out, out1, out2 = model(x) >>> print(out.shape, out1.shape, out2.shape) paddle.Size([1, 1000]) paddle.Size([1, 1000]) paddle.Size([1, 1000]) r"zJ model do not have a pretrained model now, you should set pretrained=Falserr0r)rs model_urlsrreloadset_dict) pretrainedkwargsmodelarch weight_pathparams r!r"r"s@   E Dz! f^ _ !0 t Q D!1!!4  K( u Lr )r'rFr(rFrHr )F)rrurzUnpack[_GoogLeNetOptions]rHrs)& __future__rtypingrrtyping_extensionsrrrepaddle.nn.functionalr functionalrgpaddle.base.param_attrr paddle.nnr r r r rrpaddle.nn.initializerrpaddle.utils.downloadrrpaddle._typingrr__all__rr+Layerr-rMrsr"rr r!rs# 2 ,*;$%I%   4%%P~~D,,(A,,r