ёiHSSKJr SSKJr SSKJrJrJr SSKJ r J r SSK r SSK J r SSK Jr SS KJr S S KJr \(aSS K Jr "S S\5r/rSSS.r"SS\ R.5r"SS5r"SS\ R.5r"SS\ R.5r"SS\5r"SS\5rS!S"SjjrS!S#SjjrS!S$S jjr g)%) annotations)partial) TYPE_CHECKINGCallable TypedDict) NotRequiredUnpackN)nn)get_weights_path_from_url)ConvNormActivation_make_divisible)Tensorc*\rSrSr%S\S'S\S'Srg)_MobileNetV3Options$zNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____firstlineno____annotations____static_attributes__r`/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/vision/models/mobilenetv3.pyrr$s%%$$rr)zIhttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v3_small_x1.0.pdparams 34fe0e7c1f8b00b2b056ad6788d0590c)zIhttps://paddle-hapi.bj.bcebos.com/models/mobilenet_v3_large_x1.0.pdparams 118db5792b4e183b925d8e8e334db3df)zmobilenet_v3_small_x1.0zmobilenet_v3_large_x1.0c^\rSrSrSr\R \R4SU4SjjjrSSjr SSjr Sr U=r $) SqueezeExcitation7a This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1). Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3. This code is based on the torchvision code with modifications. You can also see at https://github.com/pytorch/vision/blob/main/torchvision/ops/misc.py#L127 Args: input_channels (int): Number of channels in the input image. squeeze_channels (int): Number of squeeze channels. activation (Callable[..., paddle.nn.Layer], optional): ``delta`` activation. Default: ``paddle.nn.ReLU``. scale_activation (Callable[..., paddle.nn.Layer]): ``sigma`` activation. Default: ``paddle.nn.Sigmoid``. c>[TU]5 [R"S5Ul[R "XS5Ul[R "X!S5UlU"5UlU"5Ul g)Nr) super__init__r AdaptiveAvgPool2DavgpoolConv2Dfc1fc2 activationscale_activation)selfinput_channelssqueeze_channelsr-r. __class__s rr'SqueezeExcitation.__init__Es[ ++A. 99^qA99-qA$, 0 2rcURU5nURU5nURU5nURU5nUR U5$N)r)r+r-r,r.r/inputscales r_scaleSqueezeExcitation._scaleSsI U#&$$U++rc,URU5nX!-$r5)r9r6s rforwardSqueezeExcitation.forwardZs E"}r)r-r)r+r,r.) r0intr1r>r-Callable[..., nn.Layer]r.r?returnNone)r7rr@r) rrrr__doc__r ReLUSigmoidr'r9r<r __classcell__r2s@rr#r#7sa "/1gg46JJ 3 3 3, 3 2 3  3 3,rr#cV\rSrSrSSSjjr\SSj5rSrg)InvertedResidualConfig_c BURXS9UlX lURX8S9UlURXHS9UlXPlUcSUlOFUS:Xa[RUlO*US:Xa[RUlO[SU35eXpl g)N)r8relu hardswishz*The activation function is not supported: ) adjust_channels in_channelskernelexpanded_channels out_channelsuse_seactivation_layerr rC Hardswish RuntimeErrorstride) r/rNrOrPrQrRr-rVr8s rr'InvertedResidualConfig.__init__`s // /I !%!5!5 "6" !000K  $(D ! 6 !$&GGD ! ; &$&LLD !rOr>rPr>rQr>rRboolr-strrVr>r8float)rrrrr' staticmethodrMrrrrrHrH_sn     <44rrHcZ^\rSrSrSU4SjjrSSjrSrU=r$)InvertedResidualc >[T U]5 US:H=(a X:HUlX`lX:gUlUR(a[ UUSSSUUS9Ul[ UUUU[US- S-5UUUS9UlUR(a*[U[US-5[RS9Ul [ UUSSSUSS9Ulg)Nrr)rNrQ kernel_sizerVpadding norm_layerrSr rNrQrerVrfgroupsrgrS)r.)r&r'use_res_connectrRexpandr expand_convr>bottleneck_convr#rr Hardsigmoidmid_se linear_conv) r/rNrPrQ filter_sizerVrRrSrgr2s rr'InvertedResidual.__init__s %{J{/J !6 ;;1'.%!1 D  2)*#qQ./$!-  ;;+! 1Q 67!#DK .)%!! rc$UnUR(aURU5nURU5nUR(aUR U5nUR U5nUR (a[R"X!5nU$r5) rlrmrnrRrprqrkpaddleadd)r/xidentitys rr<InvertedResidual.forwardsn ;;  #A   # ;; AA   Q     8'Ar)rnrlrmrqrprkrR)rNr>rPr>rQr>rrr>rVr>rRr]rSr?rgr?r@rArwrr@r)rrrrr'r<rrErFs@rrbrbsk5 5 5  5  5  5 5 25 ,5  5 n  rrbcz^\rSrSr%SrS\S'S\S'S\S'S S U4S jjjrSS jrS rU=r $) MobileNetV3a+MobileNetV3 model from `"Searching for MobileNetV3" `_. Args: config (list[InvertedResidualConfig]): MobileNetV3 depthwise blocks config. last_channel (int): The number of channels on the penultimate layer. scale (float, optional): Scale of channels in each layer. Default: 1.0. num_classes (int, optional): Output dim of last fc layer. If num_classes <=0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. r_r8r>rr]rcP>[TU]5 XlX0lX lX@lXPlUSRUlUSRUl URS-Ul [[RSSS9n[SURSSS S [RUS 9Ul[R""URVs/sHYn[%URUR&UR(UR*UR,UR.UR0US 9PM[ sn6Ul[URURS S SS U[RS 9UlU(a[R6"S 5UlUS:a[R""[R:"URUR5[R"5[R<"S S9[R:"URU55Ulggs snf)NrgMbP?gGz?)epsilonmomentumr r)rNrQrerVrfrirSrg)rNrPrQrrrVrRrSrgrhg?)p) r&r'configr8 last_channelrrrNfirstconv_in_channelslastconv_in_channelslastconv_out_channelsrr BatchNorm2Dr rTconv SequentialrbrPrQrOrVrRrSblockslastconvr(r)LinearDropout classifier) r/rrr8rrrgcfgr2s rr'MobileNetV3.__init__s   (&"%+AY%:%:"$*2J$:$:!%)%>%>%B"R^^UTJ &33\\!   mm ;; 'C! #&)&;&;!$!1!1 # ::::%(%9%9) '   +1133!\\    //2DL ? mm $44d6G6GH  S! $++[9 DO ; sA H#c URU5nURU5nURU5nUR(aUR U5nUR S:a([ R"US5nURU5nU$)Nrr) rrrrr)rruflattenr)r/rws rr<MobileNetV3.forwardso IIaL KKN MM!  >> QA   a q!$A"Ar) r)rrrrrrrrrrr8rr\iT) rzlist[InvertedResidualConfig]rr>r8r_rr>rr]r@rArz) rrrrrBrr'r<rrErFs@rr|r|s|  LO  C,CC C  C  C CCJ  rr|cH^\rSrSrSrSSU4SjjjrSrU=r$)MobileNetV3Smalli-aUMobileNetV3 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:: pycon >>> 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) paddle.Size([1, 1000]) c>[SSSSSSSU5[SSSSSSSU5[SSS SSSS U5[SS S S SSSU5[S S SS SSS U5[S S SS SSS U5[S S SSSSS U5[SS SSSSS U5[SS SS SSSU5[S S SS SSS U5[S S SS SSS U5/ n[SU-S5n[TU] UUUUUS9 g)NrTrKr HFXr`(rLx0i i@irYrr8rrrHrr&r'r/r8rrrrr2s rr'MobileNetV3Small.__init__Js1 #2q"b$5 I "2q"b%E J "2q"b%E J "2q"b$ Q N "2q#r4a O "2q#r4a O "2q#r4a O "2q#r4a O "2q#r4a O "2q#r4a O "2q#r4a O  'te|Q7   %#  rrrr8r_rr>rr]r@rArrrrrBr'rrErFs@rrr-sC<         rrcH^\rSrSrSrSSU4SjjjrSrU=r$)MobileNetV3LargeigaUMobileNetV3 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:: pycon >>> 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) paddle.Size([1, 1000]) cH>[SSSSSSSU5[SSSSSSSU5[SSS SSSSU5[SS S S S SSU5[S S S S S SSU5[S S S S S SSU5[S SSSSSSU5[SSSSSSSU5[SSSSSSSU5[SSSSSSSU5[SSSSS SSU5[SSSSS SSU5[SS SSS SSU5[SS SSS SSU5[SS SSS SSU5/n[SU-S5n[TU] UUUUUS9 g)NrrFrKr@rr rrrTrrPrLr}ipiiirYrrrs rr'MobileNetV3Large.__init__s #2q"b%E J "2q"b%E J "2q"b%E J "2q"b$5 I "2q#r4E J "2q#r4E J "AsB{Au  #AsB{Au  #AsB{Au  #AsB{Au  #AsC{Au  #QS$ Q  #QS$ Q  #QS$ Q  #QS$ Q ?" F'te|Q7   %#  rrrrrrFs@rrrgsC< 0 0 0  0  0 0 rrc US:Xa[SSU0UD6nO [SSU0UD6nU(abUSU3nU[;d US35e[[US[US5n[R "U5nUR U5 U$)Nmobilenet_v3_larger8_xzJ model do not have a pretrained model now, you should set pretrained=Falserrr)rr model_urlsr ruloadset_dict)arch pretrainedr8kwargsmodel weight_pathparams r _mobilenet_v3rs  ## 7u77 7u77r%!z! f^ _ !0 t Q D!1!!4  K( u Lrc "[SXS.UD6nU$)asMobileNetV3 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:: pycon >>> 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) paddle.Size([1, 1000]) r8r)mobilenet_v3_smallrrr8rrs rrr)J  $) DJ E Lrc "[SXS.UD6nU$)asMobileNetV3 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:: pycon >>> 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) paddle.Size([1, 1000]) r)rrrs rrrrr)Fr\) rr^rr]r8r_rUnpack[_MobileNetV3Options]r@r|)rr]r8r_rrr@r)rr]r8r_rrr@r)! __future__r functoolsrtypingrrrtyping_extensionsrr rur paddle.utils.downloadr opsr _utilsrrr__all__rLayerr#rHrbr|rrrrrrrrrsQ# 2 ;$#%i%     %%P!4!4HBrxxBJb"((bJ7 {7 tM {M d  *   2(( (*( (X(( (*( (r