o + i)@sddlmZddlmZmZmZddlmZmZddl Z ddl m Z ddl m Z er@ddl m Z ddlmZmZGd d d eZgZd d d ZGddde jZ d.d/ddZgdgdgdgddZd0d$d%Z  d1d2d&d'Z  d1d2d(d)Z  d1d2d*d+Z  d1d2d,d-ZdS)3) annotations) TYPE_CHECKINGLiteral TypedDict) NotRequiredUnpackN)nn)get_weights_path_from_url)Tensor)Layer Sequentialc@seZdZUded<ded<dS) _VGGOptionszNotRequired[int] num_classeszNotRequired[bool] with_poolN)__name__ __module__ __qualname____annotations__rrd/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/vision/models/vgg.pyr !s  r )z7https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparamsZ 89bbffc0f87d260be9b8cdc169c991c4)z7https://paddle-hapi.bj.bcebos.com/models/vgg19.pdparamsZ 23b18bb13d8894f60f54e642be79a0dd)vgg16vgg19csBeZdZUdZded<ded< ddfd d ZdddZZS)VGGaDVGG model from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. Args: features (nn.Layer): Vgg features create by function make_layers. num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last three fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of VGG model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import VGG >>> from paddle.vision.models.vgg import make_layers >>> vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'] >>> features = make_layers(vgg11_cfg) # type: ignore >>> vgg11 = VGG(features) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = vgg11(x) >>> print(out.shape) [1, 1000] intrboolrTfeaturesr returnNonec s|t||_||_||_|rtd|_|dkr||}|jr ||}|jdkrt|d}||}|S)Nr)rrr"rpaddleflattenr$)r%r(rrrforwardns     z VGG.forward)rT)rr rrrrrr)r(r rr )rrr__doc__rr!r, __classcell__rrr&rr4s  rFcfglist[int | Literal['M']] batch_normrrr cCs~g}d}|D]3}|dkr|tjdddg7}qtj||ddd}|r/||t|tg7}n||tg7}|}qtj|S)NM) kernel_sizeZstrider))r5padding)rZ MaxPool2DZConv2DZ BatchNorm2Dr#r )r/r1ZlayersZ in_channelsvZconv2drrr make_layers{s r8) @r3r3r;r3r<r3r<r<r3)r9r9r3r:r:r3r;r;r3r<r<r3r<r<r3)r9r9r3r:r:r3r;r;r;r3r<r<r<r3r<r<r<r3)r9r9r3r:r:r3r;r;r;r;r3r<r<r<r<r3r<r<r<r<r3)ABDEarchstrLiteral['A', 'B', 'D', 'E'] pretrainedkwargsUnpack[_VGGOptions]cKshttt||dfi|}|r2|tvsJ|dtt|dt|d}t|}|||S)N)r1zJ model do not have a pretrained model now, you should set pretrained=Falserr))rr8cfgs model_urlsr r*load load_dict)rAr/r1rDrEmodelZ weight_pathparamrrr_vggs   rMcK&d}|r|d7}t|d||fi|S)a0VGG 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:: python >>> 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) [1, 1000] vgg11_bnr=rMrDr1rEZ model_namerrrrO#rOcKrN)a&VGG 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:: python >>> 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) [1, 1000] vgg13rPr>rQrRrrrrTrSrTcKrN)a0VGG 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:: python >>> 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) [1, 1000] rrPr?rQrRrrrrrSrcKrN)a0VGG 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:: python >>> 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) [1, 1000] rrPr@rQrRrrrr?rSr)F)r/r0r1rrr ) rArBr/rCr1rrDrrErFrr)FF)rDrr1rrErFrr) __future__rtypingrrrZtyping_extensionsrrr*rZpaddle.utils.downloadr r Z paddle.nnr r r __all__rHrr8rGrMrOrTrrrrrrsF     H   * * *