\;jh4ddlZddlmZddlmZgZdddddd d d d d dddd ZGddej ZGddej ZGddej Z dZ d&dZ d&dZ d&dZ d&dZd&dZd&dZd&dZd&d Zd&d!Zd&d"Zd&d#Zd&d$Zd&d%Zy)'N)nn)get_weights_path_from_url)z:https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams cf548f46534aa3560945be4b95cd11c4)z:https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams 8d2275cf8706028345f78ac0e1d31969)z:https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams ca6f485ee1ab0492d38f323885b0ad80)z;https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams 02f35f034ca3858e1e54d4036443c92d)z;https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams 7ad16a2f1e7333859ff986138630fd7a)zAhttps://paddle-hapi.bj.bcebos.com/models/resnext50_32x4d.pdparams dc47483169be7d6f018fcbb7baf8775d)zAhttps://paddle-hapi.bj.bcebos.com/models/resnext50_64x4d.pdparams 063d4b483e12b06388529450ad7576db)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext101_32x4d.pdparams 967b090039f9de2c8d06fe994fb9095f)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext101_64x4d.pdparams 98e04e7ca616a066699230d769d03008)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext152_32x4d.pdparams 18ff0beee21f2efc99c4b31786107121)zBhttps://paddle-hapi.bj.bcebos.com/models/resnext152_64x4d.pdparams 77c4af00ca42c405fa7f841841959379)zAhttps://paddle-hapi.bj.bcebos.com/models/wide_resnet50_2.pdparams 0282f804d73debdab289bd9fea3fa6dc)zBhttps://paddle-hapi.bj.bcebos.com/models/wide_resnet101_2.pdparams d4360a2d23657f059216f5d5a1a9ac93) resnet18resnet34resnet50 resnet101 resnet152resnext50_32x4dresnext50_64x4dresnext101_32x4dresnext101_64x4dresnext152_32x4dresnext152_64x4dwide_resnet50_2wide_resnet101_2c6eZdZdZ dfd ZdZxZS) BasicBlockc ft ||tj}|dkDr t dtj ||dd|d|_|||_tj|_ tj ||ddd|_ |||_ ||_ ||_ y)Nr!z(Dilation > 1 not supported in BasicBlockF)paddingstride bias_attr)r$r&)super__init__r BatchNorm2DNotImplementedErrorConv2Dconv1bn1ReLUreluconv2bn2 downsampler%) selfinplanesplanesr%r2groups base_widthdilation norm_layer __class__s dG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/vision/models/resnet.pyr(zBasicBlock.__init__Ps   J a<%: YY fa6U  f%GGI YYvvq!uM f%$ c|}|j|}|j|}|j|}|j|}|j |}|j |j |}||z }|j|}|SN)r,r-r/r0r1r2r3xidentityouts r;forwardzBasicBlock.forwardns{jjmhhsmiinjjohhsm ?? &q)H xiin r<r!Nr!@r!N__name__ __module__ __qualname__ expansionr(rC __classcell__r:s@r;r r Ms&I <r<r c6eZdZdZ dfd ZdZxZS)BottleneckBlockc t ||tj}t ||dz z|z} tj || dd|_|| |_tj | | d||||d|_|| |_ tj | ||jzdd|_ |||jz|_ tj|_||_||_y)NgP@r!F)r&r#)r$r%r6r8r&)r'r(rr)intr+r,r-r0r1rJconv3bn3r.r/r2r%) r3r4r5r%r2r6r7r8r9widthr:s r;r(zBottleneckBlock.__init__s   JFj4/01F:YYxUC e$YY      e$YY 6DNN*A  ft~~56GGI $ r<c|}|j|}|j|}|j|}|j|}|j |}|j|}|j |}|j |}|j|j|}||z }|j|}|Sr>)r,r-r/r0r1rRrSr2r?s r;rCzBottleneckBlock.forwardsjjmhhsmiinjjohhsmiinjjohhsm ?? &q)H xiin r<rDrFrLs@r;rNrNs'I %Nr<rNc<eZdZdZ dfd ZddZdZxZS)ResNeta.ResNet 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] cFt |gdgdgdgdgdd}||}||_||_||_||_t j|_d|_ d|_ t jd|jd d dd |_ |j|j|_ t j|_t j dd d |_|j%|d|d|_|j%|d|dd |_|j%|d|d d |_|j%|d|dd |_|rt j.d|_|dkDr)t j2d|j4z||_yy)N)rYrYrY)r#rOr#)r#rOr#)r#$r#)"2erEr!r#rYF) kernel_sizer%r$r&)rdr%r$r)r%i)r!r!)r'r(r6r7 num_classes with_poolrr) _norm_layerr4r8r+r,r-r.r/ MaxPool2Dmaxpool _make_layerlayer1layer2layer3layer4AdaptiveAvgPool2DavgpoolLinearrJfc) r3blockdepthrTrgrhr6 layer_cfglayersr:s r;r(zResNet.__init__s{   5! &">>  YY MM   ##DMM2GGI ||!QG &&ub&)< &&uc6!9Q&G &&uc6!9Q&G &&uc6!9Q&G //7DL ?iieoo 5{CDG r<c |j}d}|j}|r|xj|zc_d}|dk7s|j||jzk7rXt j t j |j||jzd|d|||jz}g} | j||j||||j|j||||jz|_td|D]<} | j||j||j|j|>t j | S)Nr!F)r%r&)r6r7r9) rir8r4rJr Sequentialr+appendr6r7range) r3rur5blocksr%dilater9r2previous_dilationrx_s r;rlzResNet._make_layers8%%   MM  MMV #MF Q;$--6EOO+CC MMU__,!# 6EOO34 J   !  0 q&!A MMMM;;#)  "}}f%%r<c|j|}|j|}|j|}|j|}|j |}|j |}|j |}|j|}|jr|j|}|jdkDr'tj|d}|j|}|S)Nrr!)r,r-r/rkrmrnrorprhrrrgpaddleflattenrt)r3r@s r;rCzResNet.forwardJs JJqM HHQK IIaL LLO KKN KKN KKN KKN >> QA   a q!$A Ar<)r`rEiTr!)r!F)rGrHrI__doc__r(rlrCrKrLs@r;rWrWs,'X.D`,&\r<rWc t||fi|}|rX|tvs J|dtt|dt|d}tj|}|j ||S)NzJ model do not have a pretrained model now, you should set pretrained=Falserr!)rW model_urlsrrloadset_dict)archBlockrv pretrainedkwargsmodel weight_pathparams r;_resnetr^s| 5% *6 *E J  _V] ^ _ / t Q D!1!!4  K( u Lr<c (tdtd|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] rr^rr rrs r;rrn< :z2z DV DDr<c (tdtd|fi|S)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] rr_rrs r;rrrr<c (tdtd|fi|S)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] rr`rrNrs r;rrs< :J I& IIr<c (tdtd|fi|S)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] rrarrs r;rr< ;j KF KKr<c (tdtd|fi|S)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] rrbrrs r;rrrr<c <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] r6rOrTrr`rrs r;rr.<F8F7O $or: P PPr<c <d|d<d|d<tdtd|fi|S)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] rEr6rOrTrr`rrs r;rr6rr<c <d|d<d|d<tdtd|fi|S)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] rr6rOrTrrarrs r;rrY5<F8F7O OS* @F r<c <d|d<d|d<tdtd|fi|S)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] rEr6rOrTrrarrs r;rr~rr<c <d|d<d|d<tdtd|fi|S)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] rr6rOrTrrbrrs r;rrrr<c <d|d<d|d<tdtd|fi|S)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] rEr6rOrTrrbrrs r;rrrr<c 2d|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] rerTrr`rrs r;rrs$<F7O $or: P PPr<c 2d|d<tdtd|fi|S)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] rerTrrarrs r;rrs+<F7O OS* @F r<)F)rrpaddle.utils.downloadr__all__rLayerr rNrWrrrrrrrrrrrrrrr<r;rs; c5 p11h>bhh>BYRXXYx  EBEBJBLBLB QF QF"J"J"J"JQD!r<