698 lines
25 KiB
Python
698 lines
25 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.nn as nn
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from paddle.utils.download import get_weights_path_from_url
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from ppdet.core.workspace import register, serializable
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from ..shape_spec import ShapeSpec
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__all__ = ['CLRResNet']
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model_urls = {
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'resnet18':
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'https://x2paddle.bj.bcebos.com/vision/models/resnet18-pt.pdparams',
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'resnet34':
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'https://x2paddle.bj.bcebos.com/vision/models/resnet34-pt.pdparams',
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'resnet50':
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'https://x2paddle.bj.bcebos.com/vision/models/resnet50-pt.pdparams',
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'resnet101':
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'https://x2paddle.bj.bcebos.com/vision/models/resnet101-pt.pdparams',
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'resnet152':
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'https://x2paddle.bj.bcebos.com/vision/models/resnet152-pt.pdparams',
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'resnext50_32x4d':
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'https://x2paddle.bj.bcebos.com/vision/models/resnext50_32x4d-pt.pdparams',
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'resnext101_32x8d':
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'https://x2paddle.bj.bcebos.com/vision/models/resnext101_32x8d-pt.pdparams',
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'wide_resnet50_2':
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'https://x2paddle.bj.bcebos.com/vision/models/wide_resnet50_2-pt.pdparams',
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'wide_resnet101_2':
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'https://x2paddle.bj.bcebos.com/vision/models/wide_resnet101_2-pt.pdparams',
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}
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class BasicBlock(nn.Layer):
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expansion = 1
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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downsample=None,
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groups=1,
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base_width=64,
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dilation=1,
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norm_layer=None):
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2D
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if dilation > 1:
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raise NotImplementedError(
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"Dilation > 1 not supported in BasicBlock")
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self.conv1 = nn.Conv2D(
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inplanes, planes, 3, padding=1, stride=stride, bias_attr=False)
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self.bn1 = norm_layer(planes)
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self.relu = nn.ReLU()
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self.conv2 = nn.Conv2D(planes, planes, 3, padding=1, bias_attr=False)
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self.bn2 = norm_layer(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class BottleneckBlock(nn.Layer):
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expansion = 4
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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downsample=None,
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groups=1,
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base_width=64,
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dilation=1,
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norm_layer=None):
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super(BottleneckBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2D
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width = int(planes * (base_width / 64.)) * groups
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self.conv1 = nn.Conv2D(inplanes, width, 1, bias_attr=False)
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self.bn1 = norm_layer(width)
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self.conv2 = nn.Conv2D(
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width,
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width,
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3,
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padding=dilation,
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stride=stride,
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groups=groups,
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dilation=dilation,
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bias_attr=False)
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self.bn2 = norm_layer(width)
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self.conv3 = nn.Conv2D(
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width, planes * self.expansion, 1, bias_attr=False)
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self.bn3 = norm_layer(planes * self.expansion)
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self.relu = nn.ReLU()
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class ResNet(nn.Layer):
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"""ResNet model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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Block (BasicBlock|BottleneckBlock): Block module of model.
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depth (int, optional): Layers of ResNet, Default: 50.
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width (int, optional): Base width per convolution group for each convolution block, Default: 64.
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num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
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will not be defined. Default: 1000.
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with_pool (bool, optional): Use pool before the last fc layer or not. Default: True.
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groups (int, optional): Number of groups for each convolution block, Default: 1.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNet model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import ResNet
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from paddle.vision.models.resnet import BottleneckBlock, BasicBlock
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# build ResNet with 18 layers
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resnet18 = ResNet(BasicBlock, 18)
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# build ResNet with 50 layers
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resnet50 = ResNet(BottleneckBlock, 50)
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# build Wide ResNet model
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wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2)
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# build ResNeXt model
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resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32)
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x = paddle.rand([1, 3, 224, 224])
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out = resnet18(x)
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print(out.shape)
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# [1, 1000]
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"""
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def __init__(self, block, depth=50, width=64, with_pool=True, groups=1):
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super(ResNet, self).__init__()
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layer_cfg = {
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18: [2, 2, 2, 2],
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34: [3, 4, 6, 3],
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50: [3, 4, 6, 3],
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101: [3, 4, 23, 3],
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152: [3, 8, 36, 3]
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}
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layers = layer_cfg[depth]
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self.groups = groups
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self.base_width = width
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self.with_pool = with_pool
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self._norm_layer = nn.BatchNorm2D
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self.inplanes = 64
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self.dilation = 1
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self.conv1 = nn.Conv2D(
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3,
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self.inplanes,
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kernel_size=7,
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stride=2,
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padding=3,
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bias_attr=False)
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self.bn1 = self._norm_layer(self.inplanes)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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if with_pool:
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self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
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ch_out_list = [64, 128, 256, 512]
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block = BottleneckBlock if depth >= 50 else BasicBlock
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self._out_channels = [block.expansion * v for v in ch_out_list]
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self._out_strides = [4, 8, 16, 32]
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self.return_idx = [0, 1, 2, 3]
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def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2D(
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self.inplanes,
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planes * block.expansion,
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1,
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stride=stride,
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bias_attr=False),
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norm_layer(planes * block.expansion), )
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layers = []
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layers.append(
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block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(
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block(
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self.inplanes,
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planes,
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groups=self.groups,
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base_width=self.base_width,
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norm_layer=norm_layer))
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return nn.Sequential(*layers)
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@property
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def out_shape(self):
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return [
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ShapeSpec(
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channels=self._out_channels[i], stride=self._out_strides[i])
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for i in self.return_idx
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]
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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out_layers = []
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x = self.layer1(x)
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out_layers.append(x)
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x = self.layer2(x)
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out_layers.append(x)
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x = self.layer3(x)
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out_layers.append(x)
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x = self.layer4(x)
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out_layers.append(x)
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if self.with_pool:
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x = self.avgpool(x)
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return out_layers
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@register
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@serializable
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class CLRResNet(nn.Layer):
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def __init__(self,
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resnet='resnet18',
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pretrained=True,
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out_conv=False,
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fea_stride=8,
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out_channel=128,
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in_channels=[64, 128, 256, 512],
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cfg=None):
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super(CLRResNet, self).__init__()
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self.cfg = cfg
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self.in_channels = in_channels
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self.model = eval(resnet)(pretrained=pretrained)
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self.out = None
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if out_conv:
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out_channel = 512
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for chan in reversed(self.in_channels):
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if chan < 0: continue
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out_channel = chan
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break
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self.out = nn.Conv2D(
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out_channel * self.model.expansion,
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cfg.featuremap_out_channel,
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kernel_size=1,
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bias_attr=False)
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@property
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def out_shape(self):
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return self.model.out_shape
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def forward(self, x):
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x = self.model(x)
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if self.out:
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x[-1] = self.out(x[-1])
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return x
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def _resnet(arch, Block, depth, pretrained, **kwargs):
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model = ResNet(Block, depth, **kwargs)
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if pretrained:
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assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
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arch)
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weight_path = get_weights_path_from_url(model_urls[arch])
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param = paddle.load(weight_path)
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model.set_dict(param)
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return model
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def resnet18(pretrained=False, **kwargs):
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"""ResNet 18-layer model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNet 18-layer model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnet18
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# build model
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model = resnet18()
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# build model and load imagenet pretrained weight
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# model = resnet18(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
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def resnet34(pretrained=False, **kwargs):
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"""ResNet 34-layer model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNet 34-layer model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnet34
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# build model
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model = resnet34()
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# build model and load imagenet pretrained weight
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# model = resnet34(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
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def resnet50(pretrained=False, **kwargs):
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"""ResNet 50-layer model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNet 50-layer model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnet50
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# build model
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model = resnet50()
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# build model and load imagenet pretrained weight
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# model = resnet50(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
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def resnet101(pretrained=False, **kwargs):
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"""ResNet 101-layer model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNet 101-layer.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnet101
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# build model
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model = resnet101()
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# build model and load imagenet pretrained weight
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# model = resnet101(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
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def resnet152(pretrained=False, **kwargs):
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"""ResNet 152-layer model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNet 152-layer model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnet152
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# build model
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model = resnet152()
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# build model and load imagenet pretrained weight
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# model = resnet152(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
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def resnext50_32x4d(pretrained=False, **kwargs):
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"""ResNeXt-50 32x4d model from
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`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 32x4d model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnext50_32x4d
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# build model
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model = resnext50_32x4d()
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# build model and load imagenet pretrained weight
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# model = resnext50_32x4d(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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kwargs['groups'] = 32
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kwargs['width'] = 4
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return _resnet('resnext50_32x4d', BottleneckBlock, 50, pretrained, **kwargs)
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def resnext50_64x4d(pretrained=False, **kwargs):
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"""ResNeXt-50 64x4d model from
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`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 64x4d model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnext50_64x4d
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# build model
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model = resnext50_64x4d()
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# build model and load imagenet pretrained weight
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# model = resnext50_64x4d(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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kwargs['groups'] = 64
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kwargs['width'] = 4
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return _resnet('resnext50_64x4d', BottleneckBlock, 50, pretrained, **kwargs)
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def resnext101_32x4d(pretrained=False, **kwargs):
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"""ResNeXt-101 32x4d model from
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`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-101 32x4d model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnext101_32x4d
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# build model
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model = resnext101_32x4d()
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# build model and load imagenet pretrained weight
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# model = resnext101_32x4d(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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kwargs['groups'] = 32
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kwargs['width'] = 4
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return _resnet('resnext101_32x4d', BottleneckBlock, 101, pretrained,
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**kwargs)
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def resnext101_64x4d(pretrained=False, **kwargs):
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"""ResNeXt-101 64x4d model from
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`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-101 64x4d model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnext101_64x4d
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# build model
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model = resnext101_64x4d()
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# build model and load imagenet pretrained weight
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# model = resnext101_64x4d(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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kwargs['groups'] = 64
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kwargs['width'] = 4
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return _resnet('resnext101_64x4d', BottleneckBlock, 101, pretrained,
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**kwargs)
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def resnext152_32x4d(pretrained=False, **kwargs):
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"""ResNeXt-152 32x4d model from
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`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-152 32x4d model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnext152_32x4d
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# build model
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model = resnext152_32x4d()
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# build model and load imagenet pretrained weight
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# model = resnext152_32x4d(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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kwargs['groups'] = 32
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kwargs['width'] = 4
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return _resnet('resnext152_32x4d', BottleneckBlock, 152, pretrained,
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**kwargs)
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def resnext152_64x4d(pretrained=False, **kwargs):
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"""ResNeXt-152 64x4d model from
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`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
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|
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Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-152 64x4d model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import resnext152_64x4d
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# build model
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model = resnext152_64x4d()
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# build model and load imagenet pretrained weight
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# model = resnext152_64x4d(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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kwargs['groups'] = 64
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kwargs['width'] = 4
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return _resnet('resnext152_64x4d', BottleneckBlock, 152, pretrained,
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**kwargs)
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def wide_resnet50_2(pretrained=False, **kwargs):
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"""Wide ResNet-50-2 model from
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`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of Wide ResNet-50-2 model.
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Examples:
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.. code-block:: python
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import paddle
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from paddle.vision.models import wide_resnet50_2
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# build model
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model = wide_resnet50_2()
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# build model and load imagenet pretrained weight
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# model = wide_resnet50_2(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
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print(out.shape)
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# [1, 1000]
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"""
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kwargs['width'] = 64 * 2
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return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)
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def wide_resnet101_2(pretrained=False, **kwargs):
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"""Wide ResNet-101-2 model from
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`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
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|
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
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|
# build model
|
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model = wide_resnet101_2()
|
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# build model and load imagenet pretrained weight
|
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# model = wide_resnet101_2(pretrained=True)
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x = paddle.rand([1, 3, 224, 224])
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out = model(x)
|
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print(out.shape)
|
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# [1, 1000]
|
|
"""
|
|
kwargs['width'] = 64 * 2
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return _resnet('wide_resnet101_2', BottleneckBlock, 101, pretrained,
|
|
**kwargs)
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