o Qe@sddlZddlmZddlmZddlmZddlmZgZ ddiZ Gd d d ej Z Gd d d ej Z dddZdddZdS)N)get_weights_path_from_url)_make_divisible)ConvNormActivationzmobilenetv2_1.0)zChttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparamsZ 0340af0a901346c8d46f4529882fb63dcs*eZdZejffdd ZddZZS)InvertedResidualc stt|||_|dvsJtt||}|jdko ||k|_g}|dkr5|t||d|t j d| t|||||t j dt j ||ddddd||gt j ||_dS)N)rrrZ kernel_size norm_layeractivation_layer)stridegroupsr r rF)Z bias_attr)superr__init__r introunduse_res_connectappendrnnReLU6extendZConv2D Sequentialconv)selfinpZoupr expand_ratior Z hidden_dimZlayers __class__PD:\Projects\ConvertPro\env\Lib\site-packages\paddle/vision/models/mobilenetv2.pyr!s6  zInvertedResidual.__init__cCs|jr |||S||S)N)rrrxrrrforwardBs zInvertedResidual.forward)__name__ __module__ __qualname__r BatchNorm2Drr! __classcell__rrrrrs!rcs*eZdZdZd fdd ZddZZS) MobileNetV2aVMobileNetV2 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] ?Tc sZtt|||_||_d}d}t}d}tj}gdgdgdgdgdgd gd g} t|||}t|t d |||_ t d |d |tj dg} | D]*\} } } }t| ||}t | D]}|dkrg|nd}| ||||| |d|}q_qN| t ||j d|tj dtj| |_|rtd|_|jdkrttdt|j ||_dSdS)N i)rrr)rr)r-r*r)r-@r)r-`r/r)r-r/r)r-i@rrr(r/r)r r r rr)rr rg?)r r'r num_classes with_poolrrr%rmax last_channelrrrangerrfeaturesZAdaptiveAvgPool2D pool2d_avgZDropoutZLinear classifier)rscaler4r5Z input_channelr7blockZ round_nearestr Zinverted_residual_settingr9tcnsZoutput_channelir rrrresr       zMobileNetV2.__init__cCs>||}|jr ||}|jdkrt|d}||}|S)Nrr)r9r5r:r4paddleflattenr;rrrrr!s     zMobileNetV2.forward)r(r)T)r"r#r$__doc__rr!r&rrrrr'Is;r'FcKsZtdi|}|r+|tvsJd|tt|dt|d}t|}|||S)NzL{} model do not have a pretrained model now, you should set pretrained=Falserrr)r' model_urlsformatrrCload load_dict)arch pretrainedkwargsmodelZ weight_pathparamrrr _mobilenets    rOr(cKs"tdt||fd|i|}|S)aqMobileNetV2 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<)rOstr)rKr<rLrMrrr mobilenet_v2s "rQ)F)Fr()rCZ paddle.nnrZpaddle.utils.downloadrutilsropsr__all__rFZLayerrr'rOrQrrrrs    * c