\;j-ddlZddlZddlmcmZddlmZddlmZddlm Z m Z m Z m Z m Z ddlmZddlmZddiZgZGd d ej(ZGd d ej(Zd ZddZy)N)nn) ParamAttr)Conv2DDropoutLinear MaxPool2DReLU)Uniform)get_weights_path_from_urlalexnet)zUhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams 7f0f9f737132e02732d75a1459d98a43c*eZdZ dfd ZdZxZS) ConvPoolLayerc t ||dk(r tnd|_t ||||||t t | |t t | ||_tddd|_ y)Nrelu initializer) in_channels out_channels kernel_sizestridepaddinggroups weight_attr bias_attrr)rrr) super__init__r rrrr _convr_pool) selfinput_channelsoutput_channels filter_sizerrstdvract __class__s eG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/vision/models/alexnet.pyrzConvPoolLayer.__init__$ss !VmDF &(#!gteT.BCGTE4,@A   1QB c|j|}|j|j|}|j|}|S)N)r rr!r"inputsxs r)forwardzConvPoolLayer.forward?s9 JJv  99  ! A JJqMr*)N)__name__ __module__ __qualname__rr/ __classcell__r(s@r)rr#s C6r*rc*eZdZdZdfd ZdZxZS)AlexNetaAlexNet model from `"ImageNet Classification with Deep Convolutional Neural Networks" `_. Args: num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. Returns: :ref:`api_paddle_nn_Layer`. An instance of AlexNet model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import AlexNet >>> alexnet = AlexNet() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = alexnet(x) >>> print(out.shape) [1, 1000] ct|||_dtjdz }t ddddd|d |_dtjd z }t dd d d d|d |_dtjdz }td ddd d tt| |tt| ||_ dtjdz }tdddd d tt| |tt| ||_ dtjdz }t dddd d |d |_ |jdkDrdtjdz }tdd|_t!ddtt| |tt| ||_tdd|_t!ddtt| |tt| ||_t!d|tt| |tt| ||_yy)Ng?ikr@ rr)r'i@r0iir)rrrri i ri$g?downscale_in_infer)pmodei) in_features out_featuresrr)rr num_classesmathsqrtr_conv1_conv2rrr _conv3_conv4_conv5r_drop1r_fc6_drop2_fc7_fc8)r"rDr&r(s r)rzAlexNet.__init__`s &TYY{++#Ar2q!TvF TYYz**#BQ1dG TYY{++   !gteT.BCGTE4,@A  TYY{++   !gteT.BCGTE4,@A  TYY{++#CaAtH   a ;//D!C.BCDK'!%'4%2FG#t0DE DI"C.BCDK !%'4%2FG#t0DE DI  (%'4%2FG#t0DE DI# r*cP|j|}|j|}|j|}tj|}|j |}tj|}|j |}|jdkDrtj|dd}|j|}|j|}tj|}|j|}|j|}tj|}|j|}|S)Nrr0) start_axis stop_axis)rGrHrIFrrJrKrDpaddleflattenrLrMrNrOrPr,s r)r/zAlexNet.forwards KK  KKN KKN FF1I KKN FF1I KKN   a qQ"=A AA ! Aq A AA ! Aq A ! Ar*)i)r1r2r3__doc__rr/r4r5s@r)r7r7Gs04lr*r7c tdi|}|rX|tvs J|dtt|dt|d}tj|}|j ||S)NzJ model do not have a pretrained model now, you should set pretrained=Falserr0)r7 model_urlsr rVload load_dict)arch pretrainedkwargsmodel weight_pathparams r)_alexnetrdsx  f E J  _V] ^ _ / t Q D!1!!4  K(  Lr*c td|fi|S)aAlexNet model from `"ImageNet Classification with Deep Convolutional Neural 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:`AlexNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of AlexNet model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import alexnet >>> # Build model >>> model = alexnet() >>> # Build model and load imagenet pretrained weight >>> # model = alexnet(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] r )rd)r_r`s r)r r s> Iz 4V 44r*)F)rErVpaddle.nn.functionalr functionalrUpaddle.base.param_attrr paddle.nnrrrrr paddle.nn.initializerr paddle.utils.downloadr r[__all__Layerrr7rdr rZr*r)rnsk ,>>);  !BHH!HbbhhbJ"5r*