226 lines
7.4 KiB
Python
226 lines
7.4 KiB
Python
import torch.nn as nn
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import torch.utils.model_zoo as model_zoo
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
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'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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def conv3x3(in_planes, out_planes, stride=1):
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"""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, bias=False)
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=nn.BatchNorm2d):
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super(BasicBlock, self).__init__()
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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = norm_layer(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = norm_layer(planes)
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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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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 Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=nn.BatchNorm2d):
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super(Bottleneck, self).__init__()
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# Both self.conv2 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv1x1(inplanes, planes)
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self.bn1 = norm_layer(planes)
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self.conv2 = conv3x3(planes, planes, stride)
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self.bn2 = norm_layer(planes)
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self.conv3 = conv1x1(planes, planes * self.expansion)
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self.bn3 = norm_layer(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 ResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, norm_layer=nn.BatchNorm2d):
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super(ResNet, self).__init__()
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self.inplanes = 64
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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 = norm_layer(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], norm_layer=norm_layer)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(512 * block.expansion, num_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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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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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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elif isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0)
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def _make_layer(self, block, planes, blocks, stride=1, norm_layer=nn.BatchNorm2d):
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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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norm_layer(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 forward(self, x):
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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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x = self.fc(x)
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return x
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def resnet18(pretrained=False, **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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model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
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return model
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def resnet34(pretrained=False, **kwargs):
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"""Constructs a ResNet-34 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, [3, 4, 6, 3], **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
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return model
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def resnet50(pretrained=False, **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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model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
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return model
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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 = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
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return model
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def resnet152(pretrained=False, **kwargs):
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"""Constructs a ResNet-152 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, 8, 36, 3], **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
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return model
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if __name__ == '__main__':
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import torch
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img = torch.randn(4, 3, 224, 224)
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model = resnet50(True)
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output = model(img) |