o QeZH@sddlmZddlmZddlmZddlZddlmZddlmZddl m Z ddl m Z d d l mZgZd d d ZGdddejZGdddZGdddejZGdddejZGdddeZGdddeZd"ddZd"ddZd"d d!ZdS)#)absolute_import)division)print_functionN)get_weights_path_from_url)partial_make_divisible)ConvNormActivation)zIhttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v3_small_x1.0.pdparamsZ 34fe0e7c1f8b00b2b056ad6788d0590c)zIhttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v3_large_x1.0.pdparamsZ 118db5792b4e183b925d8e8e334db3df)zmobilenet_v3_small_x1.0zmobilenet_v3_large_x1.0cs:eZdZdZejejffdd ZddZddZ Z S)SqueezeExcitationa This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1). Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3. This code is based on the torchvision code with modifications. You can also see at https://github.com/pytorch/vision/blob/main/torchvision/ops/misc.py#L127 Args: input_channels (int): Number of channels in the input image squeeze_channels (int): Number of squeeze channels activation (Callable[..., paddle.nn.Layer], optional): ``delta`` activation. Default: ``paddle.nn.ReLU`` scale_activation (Callable[..., paddle.nn.Layer]): ``sigma`` activation. Default: ``paddle.nn.Sigmoid`` csJttd|_t||d|_t||d|_||_||_ dS)Nr) super__init__nnAdaptiveAvgPool2DavgpoolZConv2Dfc1fc2 activationscale_activation)selfZinput_channelsZsqueeze_channelsrr __class__PD:\Projects\ConvertPro\env\Lib\site-packages\paddle/vision/models/mobilenetv3.pyr4s   zSqueezeExcitation.__init__cCs2||}||}||}||}||SN)rrrrrrinputscalerrr_scale@s     zSqueezeExcitation._scalecCs||}||Sr)rrrrrforwardGs zSqueezeExcitation.forward) __name__ __module__ __qualname____doc__rReLUZSigmoidrrr __classcell__rrrrr 's r c@s&eZdZ dddZedddZdS)InvertedResidualConfig?c Cs|j||d|_||_|j||d|_|j||d|_||_|dur&d|_n|dkr/tj|_n|dkr8tj |_nt d |||_ dS)N)rrelu hardswishz,The activation function is not supported: {}) adjust_channels in_channelskernelexpanded_channels out_channelsuse_seactivation_layerrr% Hardswish RuntimeErrorformatstride) rr,r-r.r/r0rr5rrrrrNs&    zInvertedResidualConfig.__init__cCst||dS)Nr)Zchannelsrrrrr+isz&InvertedResidualConfig.adjust_channelsN)r()r!r"r#r staticmethodr+rrrrr'Ls  r'cs$eZdZfddZddZZS)InvertedResidualc st|dko ||k|_||_||k|_|jr%t||ddd||d|_t||||t|dd|||d|_|jrHt |t |dt j d|_ t||ddd|dd|_dS)Nrr)r,r/ kernel_sizer5padding norm_layerr1r r,r/r9r5r:groupsr;r1)r)r ruse_res_connectr0expandr expand_convintbottleneck_convr r rZ Hardsigmoidmid_se linear_conv) rr,r.r/ filter_sizer5r0r1r;rrrrpsN     zInvertedResidual.__init__cCsN|}|jr ||}||}|jr||}||}|jr%t||}|Sr) r@rArCr0rDrEr?paddleadd)rxidentityrrrr s     zInvertedResidual.forward)r!r"r#rr r&rrrrr8ns )r8cs0eZdZdZ   d fdd ZddZZS) MobileNetV3a_MobileNetV3 model from `"Searching for MobileNetV3" `_. Args: config (list[InvertedResidualConfig]): MobileNetV3 depthwise blocks config. last_channel (int): The number of channels on the penultimate layer. scale (float, optional): Scale of channels in each layer. Default: 1.0. num_classes (int, optional): Output dim of last fc layer. If num_classes <=0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. r(Tc s t||_||_||_||_||_|dj|_|dj|_ |j d|_ t t j dddtd|jddd d t jd |_t jfd d |jD|_t|j |j d d dd t jd |_|rct d |_|dkrt t |j |jt t jddt |j||_dSdS)NrgMbP?gGz?)epsilonZmomentumr r)r,r/r9r5r:r=r1r;c s2g|]}t|j|j|j|j|j|j|jdqS))r,r.r/rFr5r0r1r;)r8r,r.r/r-r5r0r1).0cfgr;rr sz(MobileNetV3.__init__..r<g?)p)r rconfigr last_channel num_classes with_poolr,Zfirstconv_in_channelsZlastconv_in_channelsZlastconv_out_channelsrrZ BatchNorm2Dr r2convZ SequentialblockslastconvrrZLinearZDropout classifier)rrVrWrrXrYrrSrrsR          zMobileNetV3.__init__cCsR||}||}||}|jr||}|jdkr't|d}||}|S)Nrr) rZr[r\rYrrXrGflattenr])rrIrrrr s       zMobileNetV3.forwardr(rLT)r!r"r#r$rr r&rrrrrKs9rKc"eZdZdZdfdd ZZS)MobileNetV3SmallaMobileNetV3 Small architecture model from `"Searching for MobileNetV3" `_. 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 MobileNetV3 Small architecture model. Examples: .. code-block:: python import paddle from paddle.vision.models import MobileNetV3Small # build model model = MobileNetV3Small(scale=1.0) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] r(rLTcstddddddd|tddddddd|tddd dddd |tdd d d ddd|td d dd ddd |td d dd ddd |td d ddddd |tdd ddddd |tdd dd ddd|td d dd ddd |td d dd ddd |g }td|d}tj|||||ddS)NrPTr)r HFXr`(r*x0i i@ir6rWrrYrXr'r r rrrrXrYrVrWrrrrs&  zMobileNetV3Small.__init__r_r!r"r#r$rr&rrrrraracr`)MobileNetV3LargeaMobileNetV3 Large architecture model from `"Searching for MobileNetV3" `_. 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 MobileNetV3 Large architecture model. Examples: .. code-block:: python import paddle from paddle.vision.models import MobileNetV3Large # build model model = MobileNetV3Large(scale=1.0) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] r(rLTcsXtddddddd|tddddddd|tddd dddd|tdd d d d dd|td d d d d dd|td d d d d dd|td dddddd|tddddddd|tddddddd|tddddddd|tddddd dd|tddddd dd|tdd ddd dd|tdd ddd dd|tdd ddd dd|g}td|d}tj|||||ddS)NrbrPFr)r@rdr rcrfrhTrjriPr*ipiiir6rmrnrorrrrKsR zMobileNetV3Large.__init__r_rprrrrrr.rqrrFr(cKs|dkrtdd|i|}n tdd|i|}|rAd||}|tvs*Jd|tt|dt|d}t|}|||S)Nmobilenet_v3_largerz{}_x{}zL{} model do not have a pretrained model now, you should set pretrained=Falserrr)rrrar4 model_urlsrrGloadZset_dict)arch pretrainedrkwargsmodelZ weight_pathparamrrr _mobilenet_v3ns       rcKtd||d|}|S)aMobileNetV3 Small architecture model from `"Searching for MobileNetV3" `_. 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:`MobileNetV3Small `. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV3 Small architecture model. Examples: .. code-block:: python import paddle from paddle.vision.models import mobilenet_v3_small # build model model = mobilenet_v3_small() # build model and load imagenet pretrained weight # model = mobilenet_v3_small(pretrained=True) # build mobilenet v3 small model with scale=0.5 model = mobilenet_v3_small(scale=0.5) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] mobilenet_v3_smallrr}N)rrr}rr~rrrrr"rcKr)aMobileNetV3 Large architecture model from `"Searching for MobileNetV3" `_. 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:`MobileNetV3Large `. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV3 Large architecture model. Examples: .. code-block:: python import paddle from paddle.vision.models import mobilenet_v3_large # build model model = mobilenet_v3_large() # build model and load imagenet pretrained weight # model = mobilenet_v3_large(pretrained=True) # build mobilenet v3 large model with scale=0.5 model = mobilenet_v3_large(scale=0.5) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] ryrN)ryrrrrrryrry)Fr() __future__rrrrGZ paddle.nnrZpaddle.utils.downloadr functoolsrutilsr opsr __all__rzZLayerr r'r8rKrarrrrryrrrrs,         %"8U3 @ )