o + i$@s ddlmZddlmZmZddlmZmZddlZddl m m Z ddlm Z ddl mZddlmZmZmZmZmZmZddlmZdd lmZeradd lmZdd lmZGd d d eZgZddiZd#ddZ Gddde j!Z"Gddde j!Z#Gddde j!Z$ d$d%d"dZ%dS)&) annotations) TYPE_CHECKING TypedDict) NotRequiredUnpackN)nn) ParamAttr)AdaptiveAvgPool2D AvgPool2DConv2DDropoutLinear MaxPool2D)Uniform)get_weights_path_from_url)Tensor)Size2c@seZdZUded<ded<dS)_GoogLeNetOptionszNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____annotations__rrj/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/vision/models/googlenet.pyr+s  r googlenet)zWhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparamsZ 80c06f038e905c53ab32c40eca6e26aechannelsint filter_sizereturnrcCs*d|d|d}tt| |d}|S)Ng@g?)Z initializer)rr)rrZstdvZ param_attrrrrxavier:sr"cs.eZdZ  ddfd d ZdddZZS) ConvLayer num_channelsr num_filtersrstridergroupsc s.tt|||||dd|dd|_dS)Nr$r!F)Z in_channelsZ out_channels kernel_sizer'paddingr(Z bias_attr)super__init__r _conv)selfr%r&rr'r( __class__rrr,As   zConvLayer.__init__inputsrr cCs||}|S)N)r-)r.r1yrrrforwardUs zConvLayer.forward)r$r$) r%rr&rrrr'rr(rr1rr rrrrr,r3 __classcell__rrr/rr#@s r#cs(eZdZdfd d ZdddZZS) Inceptioninput_channelsroutput_channelsfilter1filter3Rfilter3filter5Rfilter5projc srtt||d|_t||d|_t||d|_t||d|_t||d|_tdddd|_ t||d|_ dS)Nr$)r)r'r*) r+r,r#_conv1_conv3r_conv3_conv5r_conv5r_pool_convprj) r.r8r9r:r;r<r=r>r?r/rrr,[s zInception.__init__r1rr c Csj||}||}||}||}||}||}||}tj||||gdd} t | } | S)Nr$Zaxis) rBrCrDrErFrGrHpaddleconcatFrelu) r.r1Zconv1Zconv3rZconv3Zconv5rZconv5poolZconvprjcatrrrr3qs        zInception.forward)r8rr9rr:rr;rr<rr=rr>rr?rr4r5rrr/rr7Zsr7cs@eZdZUdZded<ded<ddfd d ZdddZZS) GoogLeNetaoGoogLeNet (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) [1, 1000] [1, 1000] [1, 1000] rrboolrTr Nonec st||_||_tdddd|_tddd|_tddd|_tddd|_ t ddddd d d d |_ t d d d d dd dd|_ t d d dddd dd|_ t dddddddd|_t ddd d d ddd|_t dddddd dd|_t ddd ddd d d |_t ddd ddd d d |_t ddddddd d |_|rtd|_tddd|_tddd|_|dkr tddd|_td |td dd!|_tdd d|_td"d td#dd!|_td$dd|_ td |td dd!|_!tdd d|_"td"d td#dd!|_#td$dd|_$td |td dd!|_%dSdS)%Nr@@r!)r)r'r$` i0ipi ii@i@irArg?Zdownscale_in_infer)pmodei)Z weight_attriigffffff?)&r+r,rrr#r-rrG_conv_1_conv_2r7_ince3a_ince3b_ince4a_ince4b_ince4c_ince4d_ince4e_ince5a_ince5br _pool_5r _pool_o1_pool_o2r _dropr r"_fc_out_conv_o1_fc_o1_drop_o1_out1_conv_o2_fc_o2_drop_o2_out2)r.rrr/rrr,sD    zGoogLeNet.__init__r1rtuple[Tensor, Tensor, Tensor]c Cs||}||}||}||}||}||}||}||}||}||}||}| |}| |}||}| |}| |}|||}}}|j rj||}||}||}|jdkr||}tj|ddgd}||}||}tj|ddd}||}t|}||}||}||}tj|ddd}||}||}| |}|||fS)Nrr!r@rIr$)Z start_axisZ stop_axis)!r-rGrerfrgrhrirjrkrlrmrnrorrprqrrrrsrJZsqueezertruflattenrvrLrMrwrxryrzr{r|) r.r1xZince4aZince4dZince5boutZout1Zout2rrrr3sJ                                zGoogLeNet.forward)rRT)rrrrQr rS)r1rr r})rrr__doc__rr,r3r6rrr/rrPs 1rPF pretrainedrQkwargsUnpack[_GoogLeNetOptions]cKs^tdi|}d}|r-|tvsJ|dtt|dt|d}t|}|||S)aGoogLeNet (Inception v1) model architecture from `"Going Deeper with Convolutions" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`GoogLeNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import googlenet >>> # Build model >>> model = googlenet() >>> # Build model and load imagenet pretrained weight >>> # model = googlenet(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out, out1, out2 = model(x) >>> print(out.shape, out1.shape, out2.shape) [1, 1000] [1, 1000] [1, 1000] rzJ model do not have a pretrained model now, you should set pretrained=Falserr$Nr)rP model_urlsrrJloadZset_dict)rrmodelarchZ weight_pathparamrrrrs    )rrrrr r)F)rrQrrr rP)& __future__rtypingrrZtyping_extensionsrrrJZpaddle.nn.functionalrZ functionalrLZpaddle.base.param_attrrZ paddle.nnr r r r r rZpaddle.nn.initializerrZpaddle.utils.downloadrrZpaddle._typingrr__all__rr"ZLayerr#r7rPrrrrrs0         (