|jj*0ddlmZddlmZmZmZddlmZmZddl Z ddl m Z ddl m Z erddl m Z ddlmZmZGd d eZgZd d d ZGdde jZ d(d)dZgdgdgdgddZd*d#Z d+d,d$Z d+d,d%Z d+d,d&Z d+d,d'ZdS)-) annotations) TYPE_CHECKINGLiteral TypedDict) NotRequiredUnpackN)nn)get_weights_path_from_url)Tensor)Layer Sequentialc$eZdZUded<ded<dS) _VGGOptionszNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____annotations__h/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/vision/models/vgg.pyrr!s*%%%%$$$$$$rr)z7https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparams 89bbffc0f87d260be9b8cdc169c991c4)z7https://paddle-hapi.bj.bcebos.com/models/vgg19.pdparams 23b18bb13d8894f60f54e642be79a0dd)vgg16vgg19cFeZdZUdZded<ded< ddfd ZddZxZS)VGGagVGG model from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. Args: features (nn.Layer): Vgg features create by function make_layers. num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last three fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of VGG model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import VGG >>> from paddle.vision.models.vgg import make_layers >>> vgg11_cfg = [ ... 64, ... 'M', ... 128, ... 'M', ... 256, ... 256, ... 'M', ... 512, ... 512, ... 'M', ... 512, ... 512, ... 'M', ... ] >>> features = make_layers(vgg11_cfg) # type: ignore >>> vgg11 = VGG(features) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = vgg11(x) >>> print(out.shape) paddle.Size([1, 1000]) intrboolrTfeaturesr returnNonec t||_||_||_|rt jd|_|dkrt jt j ddt j t j t j ddt j t j t j d||_ dSdS)N)r&ribi) super__init__r"rrr AdaptiveAvgPool2Davgpoolr LinearReLUDropout classifier)selfr"rr __class__s rr(z VGG.__init__fs   &"  8/77DL ?? m +t,,   $%%   $ ,,DOOO ?rxr c||}|jr||}|jdkr*t j|d}||}|S)Nr)r"rr*rpaddleflattenr.)r/r1s rforwardz VGG.forward|sa MM!   >  QA  a  q!$$A""Ar)r!T)r"r rrrr r#r$)r1r r#r )rrr__doc__rr(r6 __classcell__)r0s@rrr4s,,\OOOKO,        rrFcfglist[int | Literal['M']] batch_normr r#r c2g}d}|D]}|dkr|tjddgz }#tj||dd}|r,||tj|tjgz }n||tjgz }|}tj|S)NM) kernel_sizestrider3)r@padding)r MaxPool2DConv2D BatchNorm2Dr,r )r9r;layers in_channelsvconv2ds r make_layersrJsFK    88 r|!<<<= =FFY{A1aHHHF .62>!#4#4bgii@@62799--KK =& !!r) @r>r>rMr>rNr>rNrNr>)rKrKr>rLrLr>rMrMr>rNrNr>rNrNr>)rKrKr>rLrLr>rMrMrMr>rNrNrNr>rNrNrNr>)rKrKr>rLrLr>rMrMrMrMr>rNrNrNrNr>rNrNrNrNr>)ABDEarchstrLiteral['A', 'B', 'D', 'E'] pretrainedkwargsUnpack[_VGGOptions]c :ttt||fi|}|rq|tvs J|dt t|dt|d}t j|}|||S)N)r;zJ model do not have a pretrained model now, you should set pretrained=Falserr3)rrJcfgs model_urlsr r4load load_dict)rSr9r;rVrWmodel weight_pathparams r_vggras  DI*=== H H H HE z!!! _ _ _"!!0 t Q D!1!!4   K((  Lrc 4d}|r|dz }t|d||fi|S)a<VGG 11-layer model from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. batch_norm (bool, optional): If True, returns a model with batch_norm layer. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG `. Returns: :ref:`api_paddle_nn_Layer`. An instance of VGG 11-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import vgg11 >>> # build model >>> model = vgg11() >>> # build vgg11 model with batch_norm >>> model = vgg11(batch_norm=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) vgg11_bnrOrarVr;rW model_names rrcrc8FJe  CZ B B6 B BBrc 4d}|r|dz }t|d||fi|S)a2VGG 13-layer model from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. batch_norm (bool): If True, returns a model with batch_norm layer. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG `. Returns: :ref:`api_paddle_nn_Layer`. An instance of VGG 13-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import vgg13 >>> # build model >>> model = vgg13() >>> # build vgg13 model with batch_norm >>> model = vgg13(batch_norm=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) vgg13rdrPrerfs rrjrjrhrc 4d}|r|dz }t|d||fi|S)a<VGG 16-layer model from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. batch_norm (bool, optional): If True, returns a model with batch_norm layer. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG `. Returns: :ref:`api_paddle_nn_Layer`. An instance of VGG 16-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import vgg16 >>> # build model >>> model = vgg16() >>> # build vgg16 model with batch_norm >>> model = vgg16(batch_norm=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrdrQrerfs rrr$rhrc 4d}|r|dz }t|d||fi|S)a<VGG 19-layer model from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. batch_norm (bool, optional): If True, returns a model with batch_norm layer. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG `. Returns: :ref:`api_paddle_nn_Layer`. An instance of VGG 19-layer model. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.vision.models import vgg19 >>> # build model >>> model = vgg19() >>> # build vgg19 model with batch_norm >>> model = vgg19(batch_norm=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) paddle.Size([1, 1000]) rrdrRrerfs rrrMrhr)F)r9r:r;r r#r ) rSrTr9rUr;r rVr rWrXr#r)FF)rVr r;r rWrXr#r) __future__rtypingrrrtyping_extensionsrrr4r paddle.utils.downloadr r paddle.nnr r r__all__r[rrJrZrarcrjrrrrrrssb#""""" 21111111 ;;;;;;%++++++++%%%%%i%%%    RRRRR"(RRRl7<"""""&            -@0&C&C&C&C&CT&C&C&C&C&CT&C&C&C&C&CT&C&C&C&C&C&C&Cr