o + iw@sddlmZddlZddlmZmZddlmZmZddl Z ddl m m Z ddl m Z ddlmZddlmZmZmZmZmZddlmZdd lmZd d iZgZeridd l mZdd lmZGdddeZGddde j Z!Gddde j Z"dddZ# d d!dd Z$dS)") annotationsN) TYPE_CHECKING TypedDict) NotRequiredUnpack)nn) ParamAttr)Conv2DDropoutLinear MaxPool2DReLU)Uniform)get_weights_path_from_urlalexnet)zUhttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparamsZ 7f0f9f737132e02732d75a1459d98a43)Tensor)Size2c@seZdZUded<dS)_AlexNetOptionszNotRequired[int] num_classesN)__name__ __module__ __qualname____annotations__rrh/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/vision/models/alexnet.pyr.s rcs.eZdZ  ddfdd ZdddZZS) ConvPoolLayerNinput_channelsintoutput_channels filter_sizerstridepaddingstdvfloatgroupsact str | NonereturnNonec sht|dkr tnd|_t||||||tt| |dtt| |dd|_tdddd|_ dS)NreluZ initializer)Z in_channelsZ out_channels kernel_sizer!r"r% weight_attr bias_attrr)r,r!r") super__init__r r*r rr_convr _pool) selfrrr r!r"r#r%r& __class__rrr23s  zConvPoolLayer.__init__inputsrcCs,||}|jdur||}||}|S)N)r3r*r4r5r8xrrrforwardNs    zConvPoolLayer.forward)rN)rrrrr rr!rr"rr#r$r%rr&r'r(r)r8rr(r)rrrr2r; __classcell__rrr6rr2s  rcs8eZdZUdZded<d dfdd Zdd d ZZS)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] rrr(r)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/@ r0r*)r&i@riir+)r!r"r-r.i i ri$g?Zdownscale_in_infer)pmodei)Z in_featuresZ out_featuresr-r.)r1r2rmathsqrtr_conv1_conv2r rr_conv3_conv4_conv5r _drop1r _fc6_drop2_fc7_fc8)r5rr#r6rrr2qsf     zAlexNet.__init__r8rcCs||}||}||}t|}||}t|}||}|jdkrStj |ddd}| |}| |}t|}| |}| |}t|}||}|S)Nrr)Z start_axisZ stop_axis)rJrKrLFr*rMrNrpaddleflattenrOrPrQrRrSr9rrrr;s"               zAlexNet.forward)r?)rrr(r)r<)rrr__doc__rr2r;r=rrr6rr>Vs 6r>archstr pretrainedboolkwargsUnpack[_AlexNetOptions]r(cKsZtdi|}|r+|tvsJ|dtt|dt|d}t|}|||S)NzJ model do not have a pretrained model now, you should set pretrained=Falserrr)r> model_urlsrrVload load_dict)rYr[r]modelZ weight_pathparamrrr_alexnets   rdFcKstd|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]rrrrs!)rYrZr[r\r]r^r(r>)F)r[r\r]r^r(r>)% __future__rrHtypingrrZtyping_extensionsrrrVZpaddle.nn.functionalrZ functionalrUZpaddle.base.param_attrrZ paddle.nnr r r r r Zpaddle.nn.initializerrZpaddle.utils.downloadrr___all__rZpaddle._typingrrZLayerrr>rdrrrrrs.       $ g