|j'`ddlmZddlmZmZddlmZmZddlZddl m cm Z ddlm Z ddl mZddlmZmZmZmZddlmZerdd lmZdd lmZGd d eZgZd ddZGdde jZGdde jZGdde jZd"dZ d#d$d Z! d#d$d!Z"dS)%) annotations) TYPE_CHECKING TypedDict) NotRequiredUnpackN)nn) ParamAttr)AdaptiveAvgPool2DConv2DDropout MaxPool2D)get_weights_path_from_url)Tensor)Size2c$eZdZUded<ded<dS)_SqueezeNetOptionszNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____annotations__o/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/vision/models/squeezenet.pyrr#s*%%%%$$$$$$rr)z[https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams 30b95af60a2178f03cf9b66cd77e1db1)z[https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams a11250d3a1f91d7131fd095ebbf09eee) squeezenet1_0 squeezenet1_1c,eZdZ ddfd Zdd ZxZS) MakeFireConvrinput_channelsintoutput_channels filter_sizerpaddingreturnNonec tt||||tt|_dS)N)r& weight_attr bias_attr)super__init__r r _conv)selfr"r$r%r& __class__s rr-zMakeFireConv.__init__7sO    ! kk     rxrcX||}tj|}|S)N)r.Frelu)r/r1s rforwardzMakeFireConv.forwardHs# JJqMM F1IIr)r) r"r#r$r#r%rr&rr'r()r1rr'rrrrr-r5 __classcell__r0s@rr!r!6s[         "rr!c(eZdZd fd Zd d ZxZS)MakeFirer"r#squeeze_channelsexpand1x1_channelsexpand3x3_channelsr'r(ctt||d|_t||d|_t||dd|_dS)N)r&)r,r-r!r. _conv_path1 _conv_path2)r/r"r;r<r=r0s rr-zMakeFire.__init__Osl !.2BAFF '(8:LaPP' 0!Q   rinputsrc||}||}||}tj||gdS)Nr?axis)r.rArBpaddleconcat)r/rCr1x1x2s rr5zMakeFire.forward]sR JJv     a    a }b"XA....r) r"r#r;r#r<r#r=r#r'r(rCrr'rr6r8s@rr:r:NsQ       ////////rr:cPeZdZUdZded<ded<ded< ddfd ZddZxZS) SqueezeNetaSqueezeNet model from `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" `_. Args: version (str): Version of SqueezeNet, which can be "1.0" or "1.1". 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 SqueezeNet model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import SqueezeNet >>> # build v1.0 model >>> model = SqueezeNet(version='1.0') >>> # build v1.1 model >>> # model = SqueezeNet(version='1.1') >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) strversionr#rboolrTr'r(c zt||_||_||_ddg}||vsJd|d||jdkrt ddddt t |_tddd |_ tdd d d |_ tdd d d |_ tdddd|_ tdddd|_tdddd|_tdddd|_tdd dd|_tdd dd|_nt dd dddt t |_tddd |_ td d d d |_ tdd d d |_ tdddd|_ tdddd|_tdddd|_tdddd|_tdd dd|_tdd dd|_t'dd|_t d|dt t |_t-d|_dS)N1.01.1zsupported versions are z but input version is r@`)strider*r+r) kernel_sizerXr&@ 0iir?)rXr&r*r+g?downscale_in_infer)pmode)r*r+)r,r-rOrrr r r.r _poolr:_conv1_conv2_conv3_conv4_conv5_conv6_conv7_conv8r _drop_conv9r _avg_pool)r/rOrrsupported_versionsr0s rr-zSqueezeNet.__init__s  &"#U^,,,, Y&8 Y YPW Y Y-,, <5 %KK#++ DJ#qAFFFDJ"2r2r22DK"3B33DK"3C55DK"3C55DK"3C55DK"3C55DK"3C55DK"3C55DKK%KK#++DJ#qAFFFDJ"2r2r22DK"3B33DK"3C55DK"3C55DK"3C55DK"3C55DK"3C55DK"3C55DKs)=>>>  aY[[IKK    +1--rrCrc||}tj|}||}|jdkr||}||}||}||}||}| |}| |}| |}||}| |}n||}||}||}||}||}||}| |}| |}| |}| |}|j dkr*||}||}|jrAtj|}||}t%j|ddg}|S)NrSrrWr@rE)r.r3r4rdrOrerfrgrhrirjrkrlrrmrnrrorGsqueeze)r/rCr1s rr5zSqueezeNet.forwards JJv   F1II JJqMM <5  AA AA AA 1 A AA AA AA AA 1 A AAA AA AA 1 A AA AA 1 A AA AA AA AA  a   1 A AA > /q Aq!!Aq1v...Ar)rQT)rOrNrr#rrPr'r(rK)rrr__doc__rr-r5r7r8s@rrMrMds@LLLOOOHL7.7.7.7.7.7.7.r""""""""rrMarchrNrO pretrainedrPkwargsUnpack[_SqueezeNetOptions]r'c t|fi|}|rq|tvs J|dtt|dt|d}tj|}|||S)NzJ model do not have a pretrained model now, you should set pretrained=Falserr?)rM model_urlsrrGloadset_dict)rtrOrurvmodel weight_pathparams r _squeezenetrs w ) )& ) )Ez!!! _ _ _"!!0 t Q D!1!!4   K(( u LrFc  tdd|fi|S)a(SqueezeNet v1.0 model from `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`SqueezeNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of SqueezeNet v1.0 model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import squeezenet1_0 >>> # build model >>> model = squeezenet1_0() >>> # build model and load imagenet pretrained weight >>> # model = squeezenet1_0(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrSrrurvs rrrB z D DV D DDrc  tdd|fi|S)a(SqueezeNet v1.1 model from `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`SqueezeNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of SqueezeNet v1.1 model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import squeezenet1_1 >>> # build model >>> model = squeezenet1_1() >>> # build model and load imagenet pretrained weight >>> # model = squeezenet1_1(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrTrrs rrrrr) rtrNrOrNrurPrvrwr'rM)F)rurPrvrwr'rM)# __future__rtypingrrtyping_extensionsrrrGpaddle.nn.functionalr functionalr3paddle.base.param_attrr paddle.nnr r r r paddle.utils.downloadrrpaddle._typingrr__all__ryLayerr!r:rMrrrrrrrs@#""""" 21111111 ,,,,,,CCCCCCCCCCCC;;;;;;%$$$$$$%%%%%Y%%%    280/////rx///,@@@@@@@@F*!E!E!E!E!EJ!E!E!E!E!E!E!Er