o Qe@sddlmZddlmZddlmZddlZddlZddlmZddlmm Z ddlm Z m Z m Z ddlmZmZddlmZddlmZdd lmZd d iZgZGd d d ejZGdddejZddZddd ZdS))absolute_import)division)print_functionN)LinearDropoutReLU)Conv2D MaxPool2D)Uniform) ParamAttr)get_weights_path_from_urlalexnet)zUhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparamsZ 7f0f9f737132e02732d75a1459d98a43cs*eZdZ  dfdd ZddZZS) ConvPoolLayerNc sltt||dkrtnd|_t||||||tt| |dtt| |dd|_t dddd|_ dS)NreluZ initializer)Z in_channelsZ out_channels kernel_sizestridepaddinggroups weight_attr bias_attrr)rrr) superr__init__rrrr r _convr _pool) selfZinput_channelsZoutput_channelsZ filter_sizerrstdvract __class__LD:\Projects\ConvertPro\env\Lib\site-packages\paddle/vision/models/alexnet.pyr*s  zConvPoolLayer.__init__cCs,||}|jdur||}||}|S)N)rrrrZinputsxr#r#r$forwardBs    zConvPoolLayer.forward)rN)__name__ __module__ __qualname__rr' __classcell__r#r#r!r$r(s  rcs*eZdZdZdfdd ZddZZS)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] c stt|||_dtd}tddddd|dd |_dtd }tdd d d d|dd |_dtd}t d ddd d t t | |dt t | |dd|_ dtd}t dddd d t t | |dt t | |dd|_ dtd}tdddd d |dd |_|jdkrdtd}tddd|_tddt t | |dt t | |dd|_tddd|_tddt t | |dt t | |dd|_td|t t | |dt t | |dd|_dSdS)Ng?ikr@ rr)r i@riir)rrrri i ri$g?Zdownscale_in_infer)pmodei)Z in_featuresZ out_featuresrr)rr,r num_classesmathsqrtr_conv1_conv2rr r _conv3_conv4_conv5r_drop1r_fc6_drop2_fc7_fc8)rr6rr!r#r$resf  zAlexNet.__init__cCs||}||}||}t|}||}t|}||}|jdkrStj |ddd}| |}| |}t|}| |}| |}t|}||}|S)Nrr)Z start_axisZ stop_axis)r9r:r;Frr<r=r6paddleflattenr>r?r@rArBr%r#r#r$r's"               zAlexNet.forward)r-)r(r)r*__doc__rr'r+r#r#r!r$r,Js1r,cKsZtdi|}|r+|tvsJd|tt|dt|d}t|}|||S)NzL{} model do not have a pretrained model now, you should set pretrained=Falserrr#)r, model_urlsformatr rEload load_dict)arch pretrainedkwargsmodelZ weight_pathparamr#r#r$_alexnets    rQFcKstd|fi|S)aAlexNet model from `"ImageNet Classification with Deep Convolutional Neural Networks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`AlexNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of AlexNet model. Examples: .. code-block:: python import paddle from paddle.vision.models import alexnet # build model model = alexnet() # build model and load imagenet pretrained weight # model = alexnet(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] r )rQ)rMrNr#r#r$r s)F) __future__rrrr7rEZ paddle.nnnnZpaddle.nn.functionalZ functionalrDrrrrr Zpaddle.nn.initializerr Zpaddle.fluid.param_attrr Zpaddle.utils.downloadr rH__all__ZLayerrr,rQr r#r#r#r$s&       "b