o + iG@s:ddlmZddlmZddlmZmZmZddlm Z m Z ddl Z ddl m Z ddl mZdd lmZd d lmZerJdd l mZGd ddeZgZdddZGddde jZGdddZGddde jZGddde jZGdddeZGdddeZ  d/d0d)d*Z  d/d1d+d,Z  d/d2d-d.Z dS)3) annotations)partial) TYPE_CHECKINGCallable TypedDict) NotRequiredUnpackN)nn)get_weights_path_from_url)ConvNormActivation_make_divisible)Tensorc@seZdZUded<ded<dS)_MobileNetV3OptionszNotRequired[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/mobilenetv3.pyr$s  r)zIhttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v3_small_x1.0.pdparamsZ 34fe0e7c1f8b00b2b056ad6788d0590c)zIhttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v3_large_x1.0.pdparamsZ 118db5792b4e183b925d8e8e334db3df)zmobilenet_v3_small_x1.0zmobilenet_v3_large_x1.0cs@eZdZdZejejfdfd d ZdddZdddZ Z S)SqueezeExcitationa This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1). Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3. This code is based on the torchvision code with modifications. You can also see at https://github.com/pytorch/vision/blob/main/torchvision/ops/misc.py#L127 Args: input_channels (int): Number of channels in the input image. squeeze_channels (int): Number of squeeze channels. activation (Callable[..., paddle.nn.Layer], optional): ``delta`` activation. Default: ``paddle.nn.ReLU``. scale_activation (Callable[..., paddle.nn.Layer]): ``sigma`` activation. Default: ``paddle.nn.Sigmoid``. input_channelsintsqueeze_channels activationCallable[..., nn.Layer]scale_activationreturnNonecsJttd|_t||d|_t||d|_||_||_ dS)Nr ) super__init__r AdaptiveAvgPool2DavgpoolZConv2Dfc1fc2rr )selfrrrr  __class__rrr$Es   zSqueezeExcitation.__init__inputrcCs2||}||}||}||}||SN)r&r'rr(r r)r,scalerrr_scaleSs     zSqueezeExcitation._scalecCs||}||Sr-)r0r.rrrforwardZs zSqueezeExcitation.forward) rrrrrrr rr!r")r,rr!r) rrr__doc__r ReLUZSigmoidr$r0r1 __classcell__rrr*rr7s rc@s(eZdZ ddddZedddZdS)InvertedResidualConfig? in_channelsrkernelexpanded_channels out_channelsuse_seboolrstrstrider/floatc Cs|j||d|_||_|j||d|_|j||d|_||_|dur&d|_n|dkr/tj|_n|dkr8tj |_nt d|||_ dS)N)r/relu hardswishz*The activation function is not supported: ) adjust_channelsr7r8r9r:r;activation_layerr r3 Hardswish RuntimeErrorr>) r)r7r8r9r:r;rr>r/rrrr$`s"    zInvertedResidualConfig.__init__cCst||dS)Nr)Zchannelsr/rrrrB~sz&InvertedResidualConfig.adjust_channelsN)r6)r7rr8rr9rr:rr;r<rr=r>rr/r?)rrrr$ staticmethodrBrrrrr5_s  r5cs(eZdZdfdd ZdddZZS)InvertedResidualr7rr9r: filter_sizer>r;r<rCr norm_layerr!r"c st|dko ||k|_||_||k|_|jr%t||ddd||d|_t||||t|dd|||d|_|jrHt |t |dt j d|_ t||ddd|dd|_dS)Nr r)r7r: kernel_sizer>paddingrJrCr r7r:rKr>rLgroupsrJrC)r )r#r$use_res_connectr;expandr expand_convrbottleneck_convrrr Z Hardsigmoidmid_se linear_conv) r)r7r9r:rIr>r;rCrJr*rrr$sN      zInvertedResidual.__init__xrcCsN|}|jr ||}||}|jr||}||}|jr%t||}|Sr-) rQrRrSr;rTrUrPpaddleadd)r)rVidentityrrrr1s     zInvertedResidual.forward)r7rr9rr:rrIrr>rr;r<rCrrJrr!r"rVrr!r)rrrr$r1r4rrr*rrHs7rHcsNeZdZUdZded<ded<ded<  ddfdd ZdddZZS) MobileNetV3aOMobileNetV3 model from `"Searching for MobileNetV3" `_. Args: config (list[InvertedResidualConfig]): MobileNetV3 depthwise blocks config. last_channel (int): The number of channels on the penultimate layer. scale (float, optional): Scale of channels in each layer. Default: 1.0. num_classes (int, optional): Output dim of last fc layer. If num_classes <=0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. r?r/rrr<rr6Tconfiglist[InvertedResidualConfig] last_channelr!r"c s t||_||_||_||_||_|dj|_|dj|_ |j d|_ t t j dddtd|jddd d t jd |_t jfd d |jD|_t|j |j d d dd t jd |_|rct d |_|dkrt t |j |jt t jddt |j||_dSdS)NrgMbP?gGz?)epsilonZmomentumr r )r7r:rKr>rLrNrCrJc s2g|]}t|j|j|j|j|j|j|jdqS))r7r9r:rIr>r;rCrJ)rHr7r9r:r8r>r;rC).0cfgrJrr s z(MobileNetV3.__init__..rMg?)p)r#r$r]r/r_rrr7Zfirstconv_in_channelsZlastconv_in_channelsZlastconv_out_channelsrr Z BatchNorm2Dr rDconvZ Sequentialblockslastconvr%r&ZLinearZDropout classifier)r)r]r_r/rrr*rfrr$sZ          zMobileNetV3.__init__rVrcCsR||}||}||}|jr||}|jdkr't|d}||}|S)Nrr ) rirjrkrr&rrWflattenrl)r)rVrrrr1s       zMobileNetV3.forwardr6r\T) r]r^r_rr/r?rrrr<r!r"rZ)rrrr2rr$r1r4rrr*rr[s  Er[c*eZdZdZ   ddfd d 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] r6r\Tr/r?rrrr<r!r"cstddddddd|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)NrcTr@r HFXr `(rAx0i i@irFr_r/rrr5rr#r$r)r/rrr]r_r*rrr$Js(  zMobileNetV3Small.__init__rnr/r?rrrr<r!r"rrrr2r$r4rrr*rrp- rpcro)MobileNetV3LargeaMobileNetV3 Large architecture model from `"Searching for MobileNetV3" `_. Args: scale (float, optional): Scale of channels in each layer. Default: 1.0. num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV3 Large architecture model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import MobileNetV3Large >>> # Build model >>> model = MobileNetV3Large(scale=1.0) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] r6r\Tr/r?rrrr<r!r"csXtddddddd|tddddddd|tddd dddd|tdd d d d dd|td d d d d dd|td d d d d dd|td dddddd|tddddddd|tddddddd|tddddddd|tddddd dd|tddddd dd|tdd ddd dd|tdd ddd dd|tdd ddd dd|g}td|d}tj|||||ddS)NrqrcFr@r @rsr rrrurwTryrxPrAipiiirFr|r}r~r*rrr$sT# zMobileNetV3Large.__init__rnrrrrr*rrgrrFr6archr= pretrainedr<r/r?kwargsUnpack[_MobileNetV3Options]r!cKs|dkrtdd|i|}n tdd|i|}|rB|d|}|tvs+J|dtt|dt|d}t|}|||S)Nmobilenet_v3_larger/Z_xzJ model do not have a pretrained model now, you should set pretrained=Falserr r)rrp model_urlsr rWloadZset_dict)rrr/rmodelZ weight_pathparamrrr _mobilenet_v3s   rcKt d||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_smallr/rN)rrrr/rrrrrr%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] rrN)rrrrrrrrr)Fr6) rr=rr<r/r?rrr!r[)rr<r/r?rrr!rp)rr<r/r?rrr!r)! __future__r functoolsrtypingrrrZtyping_extensionsrrrWr Zpaddle.utils.downloadr opsr _utilsrrr__all__rZLayerrr5rHr[rprrrrrrrrs>        ($Ee:R  ,