更换文档检测模型

paddle_detection/ppdet/modeling/backbones/clrnet_resnet.py

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+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+
+from paddle.utils.download import get_weights_path_from_url
+from ppdet.core.workspace import register, serializable
+from ..shape_spec import ShapeSpec
+
+__all__ = ['CLRResNet']
+
+model_urls = {
+    'resnet18':
+    'https://x2paddle.bj.bcebos.com/vision/models/resnet18-pt.pdparams',
+    'resnet34':
+    'https://x2paddle.bj.bcebos.com/vision/models/resnet34-pt.pdparams',
+    'resnet50':
+    'https://x2paddle.bj.bcebos.com/vision/models/resnet50-pt.pdparams',
+    'resnet101':
+    'https://x2paddle.bj.bcebos.com/vision/models/resnet101-pt.pdparams',
+    'resnet152':
+    'https://x2paddle.bj.bcebos.com/vision/models/resnet152-pt.pdparams',
+    'resnext50_32x4d':
+    'https://x2paddle.bj.bcebos.com/vision/models/resnext50_32x4d-pt.pdparams',
+    'resnext101_32x8d':
+    'https://x2paddle.bj.bcebos.com/vision/models/resnext101_32x8d-pt.pdparams',
+    'wide_resnet50_2':
+    'https://x2paddle.bj.bcebos.com/vision/models/wide_resnet50_2-pt.pdparams',
+    'wide_resnet101_2':
+    'https://x2paddle.bj.bcebos.com/vision/models/wide_resnet101_2-pt.pdparams',
+}
+
+
+class BasicBlock(nn.Layer):
+    expansion = 1
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 stride=1,
+                 downsample=None,
+                 groups=1,
+                 base_width=64,
+                 dilation=1,
+                 norm_layer=None):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2D
+
+        if dilation > 1:
+            raise NotImplementedError(
+                "Dilation > 1 not supported in BasicBlock")
+
+        self.conv1 = nn.Conv2D(
+            inplanes, planes, 3, padding=1, stride=stride, bias_attr=False)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2D(planes, planes, 3, padding=1, bias_attr=False)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class BottleneckBlock(nn.Layer):
+
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 stride=1,
+                 downsample=None,
+                 groups=1,
+                 base_width=64,
+                 dilation=1,
+                 norm_layer=None):
+        super(BottleneckBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2D
+        width = int(planes * (base_width / 64.)) * groups
+
+        self.conv1 = nn.Conv2D(inplanes, width, 1, bias_attr=False)
+        self.bn1 = norm_layer(width)
+
+        self.conv2 = nn.Conv2D(
+            width,
+            width,
+            3,
+            padding=dilation,
+            stride=stride,
+            groups=groups,
+            dilation=dilation,
+            bias_attr=False)
+        self.bn2 = norm_layer(width)
+
+        self.conv3 = nn.Conv2D(
+            width, planes * self.expansion, 1, bias_attr=False)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU()
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Layer):
+    """ResNet model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    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]
+    """
+
+    def __init__(self, block, depth=50, width=64, with_pool=True, groups=1):
+        super(ResNet, self).__init__()
+        layer_cfg = {
+            18: [2, 2, 2, 2],
+            34: [3, 4, 6, 3],
+            50: [3, 4, 6, 3],
+            101: [3, 4, 23, 3],
+            152: [3, 8, 36, 3]
+        }
+
+        layers = layer_cfg[depth]
+        self.groups = groups
+        self.base_width = width
+        self.with_pool = with_pool
+        self._norm_layer = nn.BatchNorm2D
+
+        self.inplanes = 64
+        self.dilation = 1
+
+        self.conv1 = nn.Conv2D(
+            3,
+            self.inplanes,
+            kernel_size=7,
+            stride=2,
+            padding=3,
+            bias_attr=False)
+        self.bn1 = self._norm_layer(self.inplanes)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        if with_pool:
+            self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
+
+        ch_out_list = [64, 128, 256, 512]
+        block = BottleneckBlock if depth >= 50 else BasicBlock
+
+        self._out_channels = [block.expansion * v for v in ch_out_list]
+        self._out_strides = [4, 8, 16, 32]
+        self.return_idx = [0, 1, 2, 3]
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2D(
+                    self.inplanes,
+                    planes * block.expansion,
+                    1,
+                    stride=stride,
+                    bias_attr=False),
+                norm_layer(planes * block.expansion), )
+
+        layers = []
+        layers.append(
+            block(self.inplanes, planes, stride, downsample, self.groups,
+                  self.base_width, previous_dilation, norm_layer))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    groups=self.groups,
+                    base_width=self.base_width,
+                    norm_layer=norm_layer))
+
+        return nn.Sequential(*layers)
+
+    @property
+    def out_shape(self):
+        return [
+            ShapeSpec(
+                channels=self._out_channels[i], stride=self._out_strides[i])
+            for i in self.return_idx
+        ]
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        out_layers = []
+        x = self.layer1(x)
+        out_layers.append(x)
+        x = self.layer2(x)
+        out_layers.append(x)
+        x = self.layer3(x)
+        out_layers.append(x)
+        x = self.layer4(x)
+        out_layers.append(x)
+
+        if self.with_pool:
+            x = self.avgpool(x)
+
+        return out_layers
+
+
+@register
+@serializable
+class CLRResNet(nn.Layer):
+    def __init__(self,
+                 resnet='resnet18',
+                 pretrained=True,
+                 out_conv=False,
+                 fea_stride=8,
+                 out_channel=128,
+                 in_channels=[64, 128, 256, 512],
+                 cfg=None):
+        super(CLRResNet, self).__init__()
+        self.cfg = cfg
+        self.in_channels = in_channels
+
+        self.model = eval(resnet)(pretrained=pretrained)
+        self.out = None
+        if out_conv:
+            out_channel = 512
+            for chan in reversed(self.in_channels):
+                if chan < 0: continue
+                out_channel = chan
+                break
+            self.out = nn.Conv2D(
+                out_channel * self.model.expansion,
+                cfg.featuremap_out_channel,
+                kernel_size=1,
+                bias_attr=False)
+
+    @property
+    def out_shape(self):
+        return self.model.out_shape
+
+    def forward(self, x):
+        x = self.model(x)
+        if self.out:
+            x[-1] = self.out(x[-1])
+        return x
+
+
+def _resnet(arch, Block, depth, pretrained, **kwargs):
+    model = ResNet(Block, depth, **kwargs)
+    if pretrained:
+        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
+            arch)
+        weight_path = get_weights_path_from_url(model_urls[arch])
+
+        param = paddle.load(weight_path)
+        model.set_dict(param)
+
+    return model
+
+
+def resnet18(pretrained=False, **kwargs):
+    """ResNet 18-layer model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    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 <api_paddle_vision_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]
+    """
+    return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
+
+
+def resnet34(pretrained=False, **kwargs):
+    """ResNet 34-layer model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    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 <api_paddle_vision_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]
+    """
+    return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
+
+
+def resnet50(pretrained=False, **kwargs):
+    """ResNet 50-layer model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    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 <api_paddle_vision_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]
+    """
+    return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
+
+
+def resnet101(pretrained=False, **kwargs):
+    """ResNet 101-layer model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    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 <api_paddle_vision_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]
+    """
+    return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
+
+
+def resnet152(pretrained=False, **kwargs):
+    """ResNet 152-layer model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    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 <api_paddle_vision_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]
+    """
+    return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
+
+
+def resnext50_32x4d(pretrained=False, **kwargs):
+    """ResNeXt-50 32x4d model from
+    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
+    
+    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 <api_paddle_vision_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]
+    """
+    kwargs['groups'] = 32
+    kwargs['width'] = 4
+    return _resnet('resnext50_32x4d', BottleneckBlock, 50, pretrained, **kwargs)
+
+
+def resnext50_64x4d(pretrained=False, **kwargs):
+    """ResNeXt-50 64x4d model from
+    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
+    
+    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 <api_paddle_vision_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]
+    """
+    kwargs['groups'] = 64
+    kwargs['width'] = 4
+    return _resnet('resnext50_64x4d', BottleneckBlock, 50, pretrained, **kwargs)
+
+
+def resnext101_32x4d(pretrained=False, **kwargs):
+    """ResNeXt-101 32x4d model from
+    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
+    
+    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 <api_paddle_vision_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]
+    """
+    kwargs['groups'] = 32
+    kwargs['width'] = 4
+    return _resnet('resnext101_32x4d', BottleneckBlock, 101, pretrained,
+                   **kwargs)
+
+
+def resnext101_64x4d(pretrained=False, **kwargs):
+    """ResNeXt-101 64x4d model from
+    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
+    
+    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 <api_paddle_vision_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]
+    """
+    kwargs['groups'] = 64
+    kwargs['width'] = 4
+    return _resnet('resnext101_64x4d', BottleneckBlock, 101, pretrained,
+                   **kwargs)
+
+
+def resnext152_32x4d(pretrained=False, **kwargs):
+    """ResNeXt-152 32x4d model from
+    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
+    
+    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 <api_paddle_vision_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]
+    """
+    kwargs['groups'] = 32
+    kwargs['width'] = 4
+    return _resnet('resnext152_32x4d', BottleneckBlock, 152, pretrained,
+                   **kwargs)
+
+
+def resnext152_64x4d(pretrained=False, **kwargs):
+    """ResNeXt-152 64x4d model from
+    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
+    
+    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 <api_paddle_vision_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]
+    """
+    kwargs['groups'] = 64
+    kwargs['width'] = 4
+    return _resnet('resnext152_64x4d', BottleneckBlock, 152, pretrained,
+                   **kwargs)
+
+
+def wide_resnet50_2(pretrained=False, **kwargs):
+    """Wide ResNet-50-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
+    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 <api_paddle_vision_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]
+    """
+    kwargs['width'] = 64 * 2
+    return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)
+
+
+def wide_resnet101_2(pretrained=False, **kwargs):
+    """Wide ResNet-101-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
+    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 <api_paddle_vision_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]
+    """
+    kwargs['width'] = 64 * 2
+    return _resnet('wide_resnet101_2', BottleneckBlock, 101, pretrained,
+                   **kwargs)