o + i$@sddlmZddlmZmZddlmZmZddlZddlm Z ddl m Z ddl m Z erBdd lmZdd lmZGd d d eZgZd diZGddde jZGddde jZ d"d#ddZ  d$d%d d!ZdS)&) annotations) TYPE_CHECKING TypedDict) NotRequiredUnpackN)nn)get_weights_path_from_url)ConvNormActivation)Tensor)Size2c@seZdZUded<ded<dS)_MobileNetV1OptionszNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____annotations__rrl/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/vision/models/mobilenetv1.pyr "s  r zmobilenetv1_1.0)zAhttps://paddle-hapi.bj.bcebos.com/models/mobilenetv1_1.0.pdparamsZ 3033ab1975b1670bef51545feb65fc45cs(eZdZdfd d ZdddZZS)DepthwiseSeparable in_channelsint out_channels1 out_channels2 num_groupsstrider scalefloatreturnNonec sXtt|t||d|dt||d|_tt||t||dddd|_dS)N) kernel_sizerpaddinggroupsr)r#rr$)super__init__r r_depthwise_conv_pointwise_conv)selfrrrrrr __class__rrr'3s      zDepthwiseSeparable.__init__xr cCs||}||}|S)N)r(r))r*r-rrrforwardOs  zDepthwiseSeparable.forward)rrrrrrrrrr rrrr r-r rr )rrrr'r. __classcell__rrr+rr2srcsNeZdZUdZded<ded<ded<  ddfd d ZdddZZS) MobileNetV1awMobileNetV1 model from `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" `_. Args: scale (float, optional): Scale of channels in each layer. Default: 1.0. num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV1 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import MobileNetV1 >>> model = MobileNetV1() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] rrrrboolr?Trr c sTt||_g|_||_||_tdtd|dddd|_|j t td|dddd|ddd }|j ||j t td|dd dd|dd d }|j ||j t td |d d d d|dd d }|j ||j t td |d d d d|ddd }|j ||j t td |d d d d|ddd }|j ||j t td |d dd d|ddd } |j | t dD]!} |j t td|dddd|ddt | dd } |j | q|j t td|dddd|ddd } |j | |j t td|dddd|ddd } |j | |rtd|_|dkr(ttd|||_dSdS)Nr! r r")rZ out_channelsr#rr$@)rrrrrrZconv2_1)ZsublayernameZconv2_2Zconv3_1Zconv3_2Zconv4_1iZconv4_2Zconv5_iZconv5_6Zconv6r)r&r'rdwslrrr rconv1Z add_sublayerrappendrangestrrZAdaptiveAvgPool2D pool2d_avgZLinearfc)r*rrrZdws21Zdws22Zdws31Zdws32Zdws41Zdws42itmpZdws56Zdws6r+rrr'us                      zMobileNetV1.__init__r-r cCsR||}|jD]}||}q|jr||}|jdkr't|d}||}|S)Nrr")r<r;rr@rpaddleflattenrA)r*r-Zdwsrrrr.s       zMobileNetV1.forward)r3r4T)rrrrrr2rr r/)rrr__doc__rr'r.r0rrr+rr1Us r1Farchr? pretrainedr2kwargsUnpack[_MobileNetV1Options]rcKsZtdi|}|r+|tvsJ|dtt|dt|d}t|}|||S)NzJ model do not have a pretrained model now, you should set pretrained=Falserr"r)r1 model_urlsrrDload load_dict)rGrHrImodelZ weight_pathparamrrr _mobilenets   rPr3rrcKs"tdt||fd|i|}|S)aMobileNetV1 from `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. scale (float, optional): Scale of channels in each layer. Default: 1.0. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV1 `. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV1 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import mobilenet_v1 >>> # Build model >>> model = mobilenet_v1() >>> # Build model and load imagenet pretrained weight >>> # model = mobilenet_v1(pretrained=True) >>> # build mobilenet v1 with scale=0.5 >>> model_scale = mobilenet_v1(scale=0.5) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] Z mobilenetv1_r)rPr?)rHrrIrNrrr mobilenet_v1%s& rQ)F)rGr?rHr2rIrJrr1)Fr3)rHr2rrrIrJrr1) __future__rtypingrrZtyping_extensionsrrrDrZpaddle.utils.downloadropsr r Zpaddle._typingr r __all__rKZLayerrr1rPrQrrrrs,      #@