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261
models/resnet.py
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261
models/resnet.py
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import torch.nn as nn
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import math
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import torch.utils.model_zoo as model_zoo
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import torch
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import ipdb
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
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'resnet152']
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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}
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def conv3x3(in_planes, out_planes, stride=1):
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"3x3 convolution with padding"
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = nn.BatchNorm2d(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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residual = 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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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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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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residual = 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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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1000):
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self.inplanes = 64
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super(ResNet, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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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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self.avgpool = nn.AvgPool2d(7)
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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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(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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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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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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def resnet18(args, **kwargs):
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"""Constructs a ResNet-18 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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"""
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
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if args.pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
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# modify the structure of the model.
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num_of_feature_map = model.fc.in_features
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model.fc = nn.Linear(num_of_feature_map, args.num_classes)
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model.fc.weight.data.normal_(0.0, 0.02)
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model.fc.bias.data.normal_(0)
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return model
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def resnet34(args, **kwargs):
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"""Constructs a ResNet-34 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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"""
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model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
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if args.pretrained:
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print('Load ImageNet pre-trained resnet model')
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model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
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# modify the structure of the model.
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num_of_feature_map = model.fc.in_features
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model.fc = nn.Linear(num_of_feature_map, args.num_classes)
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return model
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def resnet50(args, **kwargs):
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"""Constructs a ResNet-50 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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"""
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
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if args.pretrained:
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if args.pretrained_checkpoint: ################### use self-pretrained model
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# modify the structure of the model.
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num_of_feature_map = model.fc.in_features
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model.fc = nn.Linear(num_of_feature_map, args.num_classes * 2)
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init_dict = model.state_dict()
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pretrained_dict_temp = torch.load(args.pretrained_checkpoint)['state_dict']
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pretrained_dict = {k.replace('module.', ''): v for k, v in pretrained_dict_temp.items()}
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temp = init_dict['fc.weight'].clone()
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temp[:args.num_classes, :] = pretrained_dict['fc.weight'].clone()
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pretrained_dict['fc.weight'] = temp.clone()
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temp = init_dict['fc.bias'].clone()
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temp[:args.num_classes] = pretrained_dict['fc.bias'].clone()
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pretrained_dict['fc.bias'] = temp.clone()
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model.load_state_dict(pretrained_dict)
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else:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) ########## use imagenet pretrained model
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# modify the structure of the model.
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num_of_feature_map = model.fc.in_features
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model.fc = nn.Linear(num_of_feature_map, args.num_classes * 2)
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return model
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def resnet101(args, **kwargs):
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"""Constructs a ResNet-101 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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"""
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
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if args.pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
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# modify the structure of the model.
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num_of_feature_map = model.fc.in_features
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model.fc = nn.Linear(num_of_feature_map, args.num_classes)
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model.fc.weight.data.normal_(0.0, 0.02)
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model.fc.bias.data.normal_(0)
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return model
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def resnet152(args, **kwargs):
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"""Constructs a ResNet-152 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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"""
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model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
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if args.pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
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# modify the structure of the model.
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num_of_feature_map = model.fc.in_features
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model.fc = nn.Linear(num_of_feature_map, args.num_classes)
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return model
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def resnet(args, **kwargs): ################ Only support ResNet-50 in this simple code.
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print("==> creating model '{}' ".format(args.arch))
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if args.arch == 'resnet18':
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return resnet18(args)
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elif args.arch == 'resnet34':
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return resnet34(args)
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elif args.arch == 'resnet50':
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return resnet50(args)
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elif args.arch == 'resnet101':
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return resnet101(args)
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elif args.arch == 'resnet152':
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return resnet152(args)
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else:
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raise ValueError('Unrecognized model architecture', args.arch)
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