| from torch import nn |
| from torch.utils import model_zoo |
| |
| from torchvision.models.resnet import BasicBlock, Bottleneck |
|
|
| import torch |
| import ssl |
| |
| |
|
|
| ssl._create_default_https_context = ssl._create_unverified_context |
|
|
| all = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101','resnet152'] |
|
|
| model_urls = { |
| 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
| 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', |
| 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
| 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', |
| 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', |
| } |
|
|
|
|
| class ResNet(nn.Module): |
| def __init__(self, block, layers,classes=7,c_dim=512): |
| self.inplanes = 64 |
| super(ResNet, self).__init__() |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| bias=False) |
| self.bn1 = nn.BatchNorm2d(64) |
| self.relu = nn.ReLU(inplace=True) |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| self.layer1 = self._make_layer(block, 64, layers[0]) |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| self.avgpool = nn.AvgPool2d(7, stride=1) |
| self.class_classifier = nn.Linear(c_dim, classes) |
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
| elif isinstance(m, nn.BatchNorm2d): |
| nn.init.constant_(m.weight, 1) |
| nn.init.constant_(m.bias, 0) |
|
|
| def _make_layer(self, block, planes, blocks, stride=1): |
| downsample = None |
| if stride != 1 or self.inplanes != planes * block.expansion: |
| downsample = nn.Sequential( |
| nn.Conv2d(self.inplanes, planes * block.expansion, |
| kernel_size=1, stride=stride, bias=False), |
| nn.BatchNorm2d(planes * block.expansion), |
| ) |
|
|
| layers = [] |
| layers.append(block(self.inplanes, planes, stride, downsample)) |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes)) |
|
|
| return nn.Sequential(*layers) |
| def forward(self, x, mode='fc'): |
| if mode == 'c': |
| return self.class_classifier(x) |
| else: |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu(x) |
| x = self.maxpool(x) |
|
|
| x = self.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
| x = self.avgpool(x) |
| x = x.view(x.size(0), -1) |
| |
| return self.class_classifier(x), x |
|
|
|
|
| def resnet18(pretrained=True, **kwargs): |
| """Constructs a ResNet-18 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
| if pretrained: |
| print("-------------------------------------loading pretrain weights----------------------------------") |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) |
| return model |
|
|
| def resnet50(pretrained=True, **kwargs): |
| """Constructs a ResNet-50 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
| if pretrained: |
| print("-------------------------------------loading pretrain weights----------------------------------") |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet50']), strict=False) |
| return model |
|
|
