Spaces:
Build error
Build error
| r""" ResNet-101 backbone network """ | |
| import torch.utils.model_zoo as model_zoo | |
| import torch.nn as nn | |
| import torch | |
| __all__ = ['Backbone', 'resnet101'] | |
| 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', | |
| } | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| r""" 3x3 convolution with padding """ | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=1, groups=2, bias=False) | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| r""" 1x1 convolution """ | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, groups=2, bias=False) | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = conv1x1(inplanes, planes) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.conv2 = conv3x3(planes, planes, stride) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.conv3 = conv1x1(planes, planes * self.expansion) | |
| self.bn3 = nn.BatchNorm2d(planes * self.expansion) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| identity = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.downsample is not None: | |
| identity = self.downsample(x) | |
| out += identity | |
| out = self.relu(out) | |
| return out | |
| class Backbone(nn.Module): | |
| def __init__(self, block, layers, zero_init_residual=False): | |
| super(Backbone, self).__init__() | |
| self.inplanes = 128 | |
| self.conv1 = nn.Conv2d(6, 128, kernel_size=7, stride=2, padding=3, groups=2, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(128) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 128, layers[0]) | |
| self.layer2 = self._make_layer(block, 256, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 512, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 1024, layers[3], stride=2) | |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
| self.fc = nn.Linear(512 * block.expansion, 1000) | |
| 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) | |
| # Zero-initialize the last BN in each residual branch, | |
| # so that the residual branch starts with zeros, and each residual block behaves like an identity. | |
| # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 | |
| if zero_init_residual: | |
| for m in self.modules(): | |
| if isinstance(m, Bottleneck): | |
| nn.init.constant_(m.bn3.weight, 0) | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| conv1x1(self.inplanes, planes * block.expansion, stride), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for _ in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def resnet101(pretrained=False, **kwargs): | |
| """Constructs a ResNet-101 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = Backbone(Bottleneck, [3, 4, 23, 3], **kwargs) | |
| if pretrained: | |
| weights = model_zoo.load_url(model_urls['resnet101']) | |
| for key in weights: | |
| if key.split('.')[0] == 'fc': | |
| weights[key] = weights[key].clone() | |
| continue | |
| weights[key] = torch.cat([weights[key].clone(), weights[key].clone()], dim=0) | |
| model.load_state_dict(weights) | |
| return model | |