o #jQ@sddlZddlZddlmmZddlmZddlmZddlm Z m Z m Z m Z m Z ddlmZddlmZddiZgZGd d d ejZGd d d ejZd dZdddZdS)N)nn) ParamAttr)Conv2DDropoutLinear MaxPool2DReLU)Uniform)get_weights_path_from_urlalexnet)zUhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparamsZ 7f0f9f737132e02732d75a1459d98a43cs*eZdZ  dfdd ZddZZS) ConvPoolLayerNc sht|dkr tnd|_t||||||tt| |dtt| |dd|_tdddd|_ dS)NreluZ initializer)Z in_channelsZ out_channels kernel_sizestridepaddinggroups weight_attr bias_attrr)rrr) super__init__rrrrr _convr_pool) selfZinput_channelsZoutput_channelsZ filter_sizerrstdvract __class__]/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/vision/models/alexnet.pyr$s  zConvPoolLayer.__init__cCs,||}|jdur||}||}|S)N)rrrrZinputsxr!r!r"forward?s    zConvPoolLayer.forward)r N)__name__ __module__ __qualname__rr% __classcell__r!r!rr"r #s  r cs*eZdZdZdfdd ZddZZS)AlexNetaAlexNet model from `"ImageNet Classification with Deep Convolutional Neural Networks" `_. Args: num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. Returns: :ref:`api_paddle_nn_Layer`. An instance of AlexNet model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import AlexNet >>> alexnet = AlexNet() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = alexnet(x) >>> print(out.shape) [1, 1000] c st||_dtd}tddddd|dd |_dtd }tdd d d d|dd |_dtd}td ddd d t t | |dt t | |dd|_ dtd}tdddd d t t | |dt t | |dd|_ dtd}tdddd d |dd |_ |jdkrdtd}tddd|_tddt t | |dt t | |dd|_tddd|_tddt t | |dt t | |dd|_td|t t | |dt t | |dd|_dSdS)Ng?ikr@ rr)ri@r iir)rrrri i ri$g?Zdownscale_in_infer)pmodei)Z in_featuresZ out_featuresrr)rr num_classesmathsqrtr _conv1_conv2rrr _conv3_conv4_conv5r_drop1r_fc6_drop2_fc7_fc8)rr4rrr!r"r`sf     zAlexNet.__init__cCs||}||}||}t|}||}t|}||}|jdkrStj |ddd}| |}| |}t|}| |}| |}t|}||}|S)Nrr )Z start_axisZ stop_axis)r7r8r9Frr:r;r4paddleflattenr<r=r>r?r@r#r!r!r"r%s"               zAlexNet.forward)r+)r&r'r(__doc__rr%r)r!r!rr"r*Gs6r*cKsZtdi|}|r+|tvsJ|dtt|dt|d}t|}|||S)NzJ model do not have a pretrained model now, you should set pretrained=Falserr r!)r* model_urlsr rCload load_dict)arch pretrainedkwargsmodelZ weight_pathparamr!r!r"_alexnets   rNFcKstd|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 )rN)rJrKr!r!r"r s)F)r5rCZpaddle.nn.functionalrZ functionalrBZpaddle.base.param_attrrZ paddle.nnrrrrrZpaddle.nn.initializerr Zpaddle.utils.downloadr rF__all__ZLayerr r*rNr r!r!r!r"s    $e