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370 lines
11 KiB
370 lines
11 KiB
# 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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__all__ = [
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'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'
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]
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model_urls = {
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'resnet18': ('https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams',
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'cf548f46534aa3560945be4b95cd11c4'),
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'resnet34': ('https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams',
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'8d2275cf8706028345f78ac0e1d31969'),
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'resnet50': ('https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams',
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'ca6f485ee1ab0492d38f323885b0ad80'),
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'resnet101': ('https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams',
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'02f35f034ca3858e1e54d4036443c92d'),
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'resnet152': ('https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams',
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'7ad16a2f1e7333859ff986138630fd7a'),
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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): layers of resnet, default: 50.
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num_classes (int): 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): use pool before the last fc layer or not. Default: True.
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Examples:
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.. code-block:: python
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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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resnet50 = ResNet(BottleneckBlock, 50)
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resnet18 = ResNet(BasicBlock, 18)
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"""
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def __init__(self, block, depth, num_classes=1000, with_pool=True):
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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.num_classes = num_classes
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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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if num_classes > 0:
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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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, 1, 64,
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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(block(self.inplanes, planes, norm_layer=norm_layer))
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return nn.Sequential(*layers)
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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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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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if self.with_pool > 0:
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x = self.avgpool(x)
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if self.num_classes > 0:
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x = paddle.flatten(x, 1)
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x = self.fc(x)
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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][0],
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model_urls[arch][1])
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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
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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Examples:
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.. code-block:: python
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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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"""
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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
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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Examples:
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.. code-block:: python
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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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"""
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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
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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Examples:
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.. code-block:: python
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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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"""
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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
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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Examples:
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.. code-block:: python
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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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"""
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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
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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Examples:
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.. code-block:: python
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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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"""
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return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
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