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r""" ResNet-101 backbone network """
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import torch.utils.model_zoo as model_zoo
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import torch.nn as nn
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import torch
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__all__ = ['Backbone', 'resnet101']
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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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r""" 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, groups=2, bias=False)
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def conv1x1(in_planes, out_planes, stride=1):
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r""" 1x1 convolution """
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, groups=2, bias=False)
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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 = conv1x1(inplanes, planes)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = conv3x3(planes, planes, stride)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = conv1x1(planes, planes * self.expansion)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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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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identity = 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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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class Backbone(nn.Module):
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def __init__(self, block, layers, zero_init_residual=False):
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super(Backbone, self).__init__()
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self.inplanes = 128
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self.conv1 = nn.Conv2d(6, 128, kernel_size=7, stride=2, padding=3, groups=2,
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bias=False)
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self.bn1 = nn.BatchNorm2d(128)
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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, 128, layers[0])
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self.layer2 = self._make_layer(block, 256, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 512, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 1024, layers[3], stride=2)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(512 * block.expansion, 1000)
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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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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 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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conv1x1(self.inplanes, planes * block.expansion, stride),
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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 _ 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 resnet101(pretrained=False, **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 = Backbone(Bottleneck, [3, 4, 23, 3], **kwargs)
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if pretrained:
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weights = model_zoo.load_url(model_urls['resnet101'])
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for key in weights:
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if key.split('.')[0] == 'fc':
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weights[key] = weights[key].clone()
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continue
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weights[key] = torch.cat([weights[key].clone(), weights[key].clone()], dim=0)
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model.load_state_dict(weights)
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return model
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