o Qeg@s ddlmZddlmZddlZddlmZddlmZgZddddd d d d d ddddd Z Gdddej Z Gdddej Z Gdddej Z ddZd6ddZd6ddZd6d d!Zd6d"d#Zd6d$d%Zd6d&d'Zd6d(d)Zd6d*d+Zd6d,d-Zd6d.d/Zd6d0d1Zd6d2d3Zd6d4d5ZdS)7)division)print_functionN)get_weights_path_from_url)z:https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparamsZ cf548f46534aa3560945be4b95cd11c4)z:https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparamsZ 8d2275cf8706028345f78ac0e1d31969)z:https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparamsZ ca6f485ee1ab0492d38f323885b0ad80)z;https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparamsZ 02f35f034ca3858e1e54d4036443c92d)z;https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparamsZ 7ad16a2f1e7333859ff986138630fd7a)zAhttps://paddle-hapi.bj.bcebos.com/models/resnext50_32x4d.pdparamsZ dc47483169be7d6f018fcbb7baf8775d)zAhttps://paddle-hapi.bj.bcebos.com/models/resnext50_64x4d.pdparamsZ 063d4b483e12b06388529450ad7576db)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext101_32x4d.pdparamsZ 967b090039f9de2c8d06fe994fb9095f)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext101_64x4d.pdparamsZ 98e04e7ca616a066699230d769d03008)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext152_32x4d.pdparamsZ 18ff0beee21f2efc99c4b31786107121)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext152_64x4d.pdparamsZ 77c4af00ca42c405fa7f841841959379)zAhttps://paddle-hapi.bj.bcebos.com/models/wide_resnet50_2.pdparamsZ 0282f804d73debdab289bd9fea3fa6dc)zBhttps://paddle-hapi.bj.bcebos.com/models/wide_resnet101_2.pdparamsZ d4360a2d23657f059216f5d5a1a9ac93) resnet18resnet34resnet50 resnet101 resnet152resnext50_32x4dresnext50_64x4dresnext101_32x4dresnext101_64x4dresnext152_32x4dresnext152_64x4dwide_resnet50_2wide_resnet101_2cs6eZdZdZ      dfdd ZddZZS) BasicBlockN@c stt||durtj}|dkrtdtj||dd|dd|_|||_t |_ tj||dddd|_ |||_ ||_ ||_dS)Nrz(Dilation > 1 not supported in BasicBlockF)paddingstride bias_attr)rr)superr__init__nn BatchNorm2DNotImplementedErrorConv2Dconv1bn1ReLUreluconv2bn2 downsampler) selfinplanesplanesrr%groups base_widthdilation norm_layer __class__KD:\Projects\ConvertPro\env\Lib\site-packages\paddle/vision/models/resnet.pyrBs(     zBasicBlock.__init__cCs`|}||}||}||}||}||}|jdur%||}||7}||}|SN)rr r"r#r$r%r&xidentityoutr/r/r0forward`s        zBasicBlock.forwardrNrrrN__name__ __module__ __qualname__ expansionrr6 __classcell__r/r/r-r0r?srcs6eZdZdZ      d fdd ZddZZS) BottleneckBlockrNrc stt||durtj}t||d|} tj|| ddd|_|| |_tj| | d||||dd|_ || |_ tj| ||j ddd|_ |||j |_ t|_||_||_dS)NgP@rF)rr)rrr)r+r)rr>rrrintrrr r#r$r<conv3bn3r!r"r%r) r&r'r(rr%r)r*r+r,widthr-r/r0rws2     zBottleneckBlock.__init__cCs~|}||}||}||}||}||}||}||}||}|jdur4||}||7}||}|Sr1)rr r"r#r$rArBr%r2r/r/r0r6s           zBottleneckBlock.forwardr7r8r/r/r-r0r>ss$r>cs>eZdZdZ     dfdd Zdd d Zd d ZZS)ResNetaResNet model from `"Deep Residual Learning for Image Recognition" `_. Args: Block (BasicBlock|BottleneckBlock): Block module of model. depth (int, optional): Layers of ResNet, Default: 50. width (int, optional): Base width per convolution group for each convolution block, Default: 64. num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. groups (int, optional): Number of groups for each convolution block, Default: 1. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNet model. Examples: .. code-block:: python import paddle from paddle.vision.models import ResNet from paddle.vision.models.resnet import BottleneckBlock, BasicBlock # build ResNet with 18 layers resnet18 = ResNet(BasicBlock, 18) # build ResNet with 50 layers resnet50 = ResNet(BottleneckBlock, 50) # build Wide ResNet model wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2) # build ResNeXt model resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32) x = paddle.rand([1, 3, 224, 224]) out = resnet18(x) print(out.shape) # [1, 1000] 2rTrc s:tt|gdgdgdgdgdd}||}||_||_||_||_tj|_ d|_ d|_ tj d|j d d dd d |_ | |j |_t|_tjdd dd |_||d|d|_|j|d|dd d|_|j|d|d d d|_|j|d|dd d|_|rtd|_|dkrtd|j||_dSdS)N)rGrGrG)rr?r)rr?r)r$r)"rEerrrrGF) kernel_sizerrr)rQrrr)ri)rr)rrDrr)r* num_classes with_poolrr _norm_layerr'r+rrr r!r"Z MaxPool2Dmaxpool _make_layerlayer1layer2layer3layer4ZAdaptiveAvgPool2DavgpoolZLinearr<fc) r&blockdepthrCrTrUr)Z layer_cfglayersr-r/r0rsD  zResNet.__init__Fc Cs|j}d}|j}|r|j|9_d}|dks|j||jkr6ttj|j||jd|dd|||j}g} | ||j||||j|j ||||j|_t d|D]} | ||j||j|j |dqTtj| S)NrF)rr)r)r*r,) rVr+r'r<rZ Sequentialrappendr)r*range) r&r_r(blocksrZdilater,r%Zprevious_dilationra_r/r/r0rXsB    zResNet._make_layercCs||}||}||}||}||}||}||}||}|jr0| |}|j dkr@t |d}| |}|S)Nrr)rr r"rWrYrZr[r\rUr]rTpaddleflattenr^)r&r3r/r/r0r6(s            zResNet.forward)rErrFTr)rF)r9r:r;__doc__rrXr6r=r/r/r-r0rDs+ , rDcKs^t||fi|}|r-|tvsJd|tt|dt|d}t|}|||S)NzL{} model do not have a pretrained model now, you should set pretrained=Falserr)rD model_urlsformatrrfloadZset_dict)archZBlockr` pretrainedkwargsmodelZ weight_pathparamr/r/r0_resnet<s    rqFcKtdtd|fi|S)aResNet 18-layer model from `"Deep Residual Learning for Image Recognition" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNet 18-layer model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnet18 # build model model = resnet18() # build model and load imagenet pretrained weight # model = resnet18(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rrLrqrrmrnr/r/r0rJrcKrr)aResNet 34-layer model from `"Deep Residual Learning for Image Recognition" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNet 34-layer model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnet34 # build model model = resnet34() # build model and load imagenet pretrained weight # model = resnet34(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rrMrsrtr/r/r0rkrurcKrr)aResNet 50-layer model from `"Deep Residual Learning for Image Recognition" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNet 50-layer model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnet50 # build model model = resnet50() # build model and load imagenet pretrained weight # model = resnet50(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rrErqr>rtr/r/r0rrurcKrr)aResNet 101-layer model from `"Deep Residual Learning for Image Recognition" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNet 101-layer. Examples: .. code-block:: python import paddle from paddle.vision.models import resnet101 # build model model = resnet101() # build model and load imagenet pretrained weight # model = resnet101(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rrNrvrtr/r/r0rrurcKrr)aResNet 152-layer model from `"Deep Residual Learning for Image Recognition" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNet 152-layer model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnet152 # build model model = resnet152() # build model and load imagenet pretrained weight # model = resnet152(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] r rOrvrtr/r/r0r rur cK&d|d<d|d<tdtd|fi|S)aResNeXt-50 32x4d model from `"Aggregated Residual Transformations for Deep Neural Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 32x4d model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnext50_32x4d # build model model = resnext50_32x4d() # build model and load imagenet pretrained weight # model = resnext50_32x4d(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] r)r?rCr rErvrtr/r/r0r r cKrw)aResNeXt-50 64x4d model from `"Aggregated Residual Transformations for Deep Neural Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 64x4d model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnext50_64x4d # build model model = resnext50_64x4d() # build model and load imagenet pretrained weight # model = resnext50_64x4d(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rr)r?rCr rErvrtr/r/r0r ryr cKrw)aResNeXt-101 32x4d model from `"Aggregated Residual Transformations for Deep Neural Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-101 32x4d model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnext101_32x4d # build model model = resnext101_32x4d() # build model and load imagenet pretrained weight # model = resnext101_32x4d(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rxr)r?rCr rNrvrtr/r/r0r 5 r cKrw)aResNeXt-101 64x4d model from `"Aggregated Residual Transformations for Deep Neural Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-101 64x4d model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnext101_64x4d # build model model = resnext101_64x4d() # build model and load imagenet pretrained weight # model = resnext101_64x4d(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rr)r?rCr rNrvrtr/r/r0r Yrzr cKrw)aResNeXt-152 32x4d model from `"Aggregated Residual Transformations for Deep Neural Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-152 32x4d model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnext152_32x4d # build model model = resnext152_32x4d() # build model and load imagenet pretrained weight # model = resnext152_32x4d(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rxr)r?rCrrOrvrtr/r/r0r}rzrcKrw)aResNeXt-152 64x4d model from `"Aggregated Residual Transformations for Deep Neural Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-152 64x4d model. Examples: .. code-block:: python import paddle from paddle.vision.models import resnext152_64x4d # build model model = resnext152_64x4d() # build model and load imagenet pretrained weight # model = resnext152_64x4d(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rr)r?rCrrOrvrtr/r/r0rrzrcKd|d<tdtd|fi|S)aWide ResNet-50-2 model from `"Wide Residual Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of Wide ResNet-50-2 model. Examples: .. code-block:: python import paddle from paddle.vision.models import wide_resnet50_2 # build model model = wide_resnet50_2() # build model and load imagenet pretrained weight # model = wide_resnet50_2(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rRrCrrErvrtr/r/r0rsrcKr{)aWide ResNet-101-2 model from `"Wide Residual Networks" `_. 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:`ResNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of Wide ResNet-101-2 model. Examples: .. code-block:: python import paddle from paddle.vision.models import wide_resnet101_2 # build model model = wide_resnet101_2() # build model and load imagenet pretrained weight # model = wide_resnet101_2(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] rRrCrrNrvrtr/r/r0rsr)F) __future__rrrfZ paddle.nnrZpaddle.utils.downloadr__all__riZLayerrr>rDrqrrrrr r r r r rrrrr/r/r/r0sL    &4?  ! ! ! ! ! # # $ $ $ $"