ёi$6SSKJr SSKJrJr SSKJrJr SSKrSSKJ r SSK J r SSK J r \(aSS KJr SS KJr "S S \5r/rS S0r"SS\ R(5r"SS\ R(5rSSSjjrSSSjjrg)) annotations) TYPE_CHECKING TypedDict) NotRequiredUnpackN)nn)get_weights_path_from_url)ConvNormActivation)Tensor)Size2c*\rSrSr%S\S'S\S'Srg)_MobileNetV1Options"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/mobilenetv1.pyrr"s%%$$rrzmobilenetv1_1.0)zAhttps://paddle-hapi.bj.bcebos.com/models/mobilenetv1_1.0.pdparams 3033ab1975b1670bef51545feb65fc45cR^\rSrSrSU4SjjrSSjrSrU=r$)DepthwiseSeparable2c >[TU]5 [U[X&-5SUS[XF-5S9Ul[[X&-5[X6-5SSSS9Ulg)N) kernel_sizestridepaddinggroupsr)r#r$r%)super__init__r int_depthwise_conv_pointwise_conv)self in_channels out_channels1 out_channels2 num_groupsr$scale __class__s rr(DepthwiseSeparable.__init__3sm 1   % &z)*   2  % &  % &  rcJURU5nURU5nU$)Nr*r+)r,xs rforwardDepthwiseSeparable.forwardOs'   #   #rr5)r-r)r.r)r/r)r0r)r$r r1floatreturnNoner6r r:r )rrrrr(r7r __classcell__r2s@rrr2sV            8rrcr^\rSrSr%SrS\S'S\S'S\S'S S U4S jjjrSS jrS rU=r $) MobileNetV1Ua;MobileNetV1 model from `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" `_. 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 MobileNetV1 model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import MobileNetV1 >>> model = MobileNetV1() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) r9r1r)rboolrc *>[TU]5 Xl/UlX lX0l[ S[SU-5SSSS9UlUR[[SU-5SSSSUS9SS 9nURRU5 UR[[SU-5SS SSUS9S S 9nURRU5 UR[[S U-5S S S SUS9S S 9nURRU5 UR[[S U-5S S S SUS9SS 9nURRU5 UR[[S U-5S S S SUS9SS 9nURRU5 UR[[S U-5S SS SUS9SS 9n URRU 5 [S5HUn UR[[SU-5SSSSUS9S[U S-5-S 9n URRU 5 MW UR[[SU-5SSSSUS9SS 9n URRU 5 UR[[SU-5SSSSUS9SS 9n URRU 5 U(a[R"S5UlUS:a)[R""[SU-5U5Ulgg)Nr! r r")r- out_channelsr#r$r%@)r-r.r/r0r$r1conv2_1)sublayernameconv2_2conv3_1conv3_2conv4_1iconv4_2conv5_iconv5_6conv6r)r'r(r1dwslrrr r)conv1 add_sublayerrappendrangestrrAdaptiveAvgPool2D pool2d_avgLinearfc)r,r1rrdws21dws22dws31dws32dws41dws42itmpdws56dws6r2s rr(MobileNetV1.__init__us|   &"'R%Z   !!'U O   "   !!'U O ! "   !!'e ,!! "   !!'e ,!! "   !!'e ,!! "   !!'e ,!! "   qA##+ #C%K 0"%"%" AE *$ C II  S !!!'e ,!" "     'u -"" !     2215DO ?iiD5L 1;?DG rcURU5nURH nU"U5nM UR(aURU5nURS:a([ R "US5nURU5nU$)Nrr")rVrUrr\rpaddleflattenr^)r,r6dwss rr7MobileNetV1.forwardsm JJqM99CAA >>"A   a q!$A Ar)rVrUr^rr\r1r)?iT)r1r9rr)rrBr:r;r<) rrrr__doc__rr(r7rr=r>s@rr@r@Usl6 LO N@N@N@ N@  N@N@`  rr@c [S0UD6nU(a[U[;d US35e[[US[US5n[R"U5nUR U5 U$)NzJ model do not have a pretrained model now, you should set pretrained=Falserr"r)r@ model_urlsr rkload load_dict)arch pretrainedkwargsmodel weight_pathparams r _mobilenetr{sz  !& !Ez! f^ _ !0 t Q D!1!!4  K(  Lrc <[S[U5-U4SU0UD6nU$)a`MobileNetV1 from `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" `_. 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:`MobileNetV1 `. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV1 model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import mobilenet_v1 >>> # Build model >>> model = mobilenet_v1() >>> # Build model and load imagenet pretrained weight >>> # model = mobilenet_v1(pretrained=True) >>> # build mobilenet v1 with scale=0.5 >>> model_scale = mobilenet_v1(scale=0.5) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) mobilenetv1_r1)r{rZ)rvr1rwrxs r mobilenet_v1r~%s4L U#Z 7< @F E Lr)F)rurZrvrBrwUnpack[_MobileNetV1Options]r:r@)Fro)rvrBr1r9rwrr:r@) __future__rtypingrrtyping_extensionsrrrkrpaddle.utils.downloadr opsr r paddle._typingr r__all__rrLayerrr@r{r~rrrrs# 2 ;$$%i%    F{"(({~#( 3N&)) )*) )r