| | 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 |
| |
|
