o #Õj®&ã@sœddlZddlmZddlmZgZdddœZGdd„dejƒZdd d „Zgd ¢gd ¢gd¢gd¢dœZ dd„Z ddd„Z ddd„Z ddd„Z ddd„ZdS)éN)Únn)Úget_weights_path_from_url)z7https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparamsZ 89bbffc0f87d260be9b8cdc169c991c4)z7https://paddle-hapi.bj.bcebos.com/models/vgg19.pdparamsZ 23b18bb13d8894f60f54e642be79a0dd)Úvgg16Úvgg19cs*eZdZdZd‡fdd„ Zdd„Z‡ZS) ÚVGGa4VGG 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) >>> vgg11 = VGG(features) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = vgg11(x) >>> print(out.shape) [1, 1000] éèTc s|tƒ ¡||_||_||_|rt d¡|_|dkr| |¡}|jr | |¡}|jdkrt |d¡}| |¡}|S)Nré)r r rr ÚpaddleÚflattenr)rÚxrrrÚforwardVs     z VGG.forward)rT)Ú__name__Ú __module__Ú __qualname__Ú__doc__r rÚ __classcell__rrrrr!s rFcCs~g}d}|D]3}|dkr|tjdddg7}qtj||ddd}|r/||t |¡t ¡g7}n||t ¡g7}|}qtj|ŽS)NéÚMé)Ú kernel_sizeZstrider)r$Úpadding)rZ MaxPool2DZConv2DZ BatchNorm2Drr)ÚcfgÚ batch_normZlayersZ in_channelsÚvZconv2drrrÚ make_layerscs r)) é@r"é€r"ér,r"ér-r"r-r-r")r*r*r"r+r+r"r,r,r"r-r-r"r-r-r")r*r*r"r+r+r"r,r,r,r"r-r-r-r"r-r-r-r")r*r*r"r+r+r"r,r,r,r,r"r-r-r-r-r"r-r-r-r-r")ÚAÚBÚDÚEcKshttt||dfi|¤Ž}|r2|tvsJ|›dƒ‚tt|dt|dƒ}t |¡}| |¡|S)N)r'zJ model do not have a pretrained model now, you should set pretrained=Falserr)rr)ÚcfgsÚ model_urlsrrÚloadÚ load_dict)Úarchr&r'Ú pretrainedÚkwargsÚmodelZ weight_pathÚparamrrrÚ_vgg´s ÿÿ  r;cKó&d}|r|d7}t|d||fi|¤ŽS)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] Úvgg11Ú_bnr.©r;©r7r'r8Z model_namerrrr=Åór=cKr<)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] Úvgg13r>r/r?r@rrrrBêrArBcKr<)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] rr>r0r?r@rrrrrArcKr<)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] rr>r1r?r@rrrr4rAr)F)FF)rrZpaddle.utils.downloadrÚ__all__r3ZLayerrr)r2r;r=rBrrrrrrÚs&  û BÙA  % %%