o + iB@sddlmZddlmZmZmZddlmZmZddl Z ddl m Z ddl m Z ddl mZd d lmZerDdd l mZGd d d eZgZddiZGddde jZGddde jZ d#d$ddZ  d%d&d!d"ZdS)') annotations) TYPE_CHECKINGCallable TypedDict) NotRequiredUnpackN)nn)get_weights_path_from_url)ConvNormActivation)_make_divisible)Tensorc@seZdZUded<ded<dS)_MobileNetV2OptionszNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____annotations__rrl/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/vision/models/mobilenetv2.pyr#s  rzmobilenetv2_1.0)zChttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparamsZ 0340af0a901346c8d46f4529882fb63dcs.eZdZejfdfd d ZdddZZS)InvertedResidualinpintoupstride expand_ratiofloat norm_layerCallable[..., nn.Layer]returnNonec st||_|dvsJtt||}|jdko||k|_g}|dkr3|t||d|tj d| t|||||tj dtj ||ddddd||gtj ||_ dS)N)r r r Z kernel_sizeractivation_layer)rgroupsrr$rF)Z bias_attr)super__init__rrrounduse_res_connectappendr rReLU6extendZConv2D Sequentialconv)selfrrrrrZ hidden_dimZlayers __class__rrr'3s<   zInvertedResidual.__init__xrcCs|jr |||S||S)N)r)r.r/r2rrrforward]s zInvertedResidual.forward) rrrrrrrrrr r!r"r2rr!r)rrrr BatchNorm2Dr'r4 __classcell__rrr0rr2s*rcsFeZdZUdZded<ded<   ddfd d ZdddZZS) MobileNetV2a[MobileNetV2 model from `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" `_. 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 MobileNetV2 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import MobileNetV2 >>> model = MobileNetV2() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] rrboolr?Tscalerr!r"c sVt||_||_d}d}t}d}tj}gdgdgdgdgdgd gd g} t|||}t|td |||_ t d |d |tj dg} | D]*\} } } }t| ||}t | D]}|dkre|nd}| ||||| |d|}q]qL| t ||j d|tj dtj| |_|rtd|_|jdkrttdt|j ||_dSdS)N i)r r r )r r )r@r=r )r@@r )r@`rBr )r@rBr )r@i@r r r:rBr )rrr$rr )rrr#g?)r&r'rrrrr6r max last_channelr r+ranger*r-featuresZAdaptiveAvgPool2D pool2d_avgZDropoutZLinear classifier)r/r<rrZ input_channelrHblockZ round_nearestrZinverted_residual_settingrJtcnsZoutput_channelirr0rrr'sx          zMobileNetV2.__init__r2rcCs>||}|jr ||}|jdkrt|d}||}|S)Nrr )rJrrKrpaddleflattenrLr3rrrr4s     zMobileNetV2.forward)r:r;T)r<rrrrr9r!r"r5)rrr__doc__rr'r4r7rrr0rr8ds Jr8Farchstr pretrainedr9kwargsUnpack[_MobileNetV2Options]r!cKsZtdi|}|r+|tvsJ|dtt|dt|d}t|}|||S)NzJ model do not have a pretrained model now, you should set pretrained=Falserr r)r8 model_urlsr rSload load_dict)rVrXrYmodelZ weight_pathparamrrr _mobilenets   r`r:r<rcKs"tdt||fd|i|}|S)aMobileNetV2 from `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" `_. 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:`MobileNetV2 `. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV2 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import mobilenet_v2 >>> # Build model >>> model = mobilenet_v2() >>> # Build model and load imagenet pretrained weight >>> # model = mobilenet_v2(pretrained=True) >>> # Build mobilenet v2 with scale=0.5 >>> model = mobilenet_v2(scale=0.5) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] Z mobilenetv2_r<)r`rW)rXr<rYr^rrr mobilenet_v2s% ra)F)rVrWrXr9rYrZr!r8)Fr:)rXr9r<rrYrZr!r8) __future__rtypingrrrZtyping_extensionsrrrSrZpaddle.utils.downloadr opsr _utilsr rr__all__r[ZLayerrr8r`rarrrrs*      2v