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from torch import nn |
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from torch.utils import model_zoo |
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from torchvision.models.resnet import BasicBlock, Bottleneck |
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import torch |
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import ssl |
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ssl._create_default_https_context = ssl._create_unverified_context |
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all = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101','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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class ResNet(nn.Module): |
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def __init__(self, block, layers,classes=7,c_dim=512): |
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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, stride=1) |
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self.class_classifier = nn.Linear(c_dim, classes) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, nn.BatchNorm2d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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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, mode='fc'): |
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if mode == 'c': |
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return self.class_classifier(x) |
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else: |
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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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return self.class_classifier(x), x |
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def resnet18(pretrained=True, **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 pretrained: |
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print("-------------------------------------loading pretrain weights----------------------------------") |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) |
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return model |
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def resnet50(pretrained=True, **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 pretrained: |
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print("-------------------------------------loading pretrain weights----------------------------------") |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet50']), strict=False) |
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return model |
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