import os
import torch.nn as nn
from torch.hub import load_state_dict_from_url
from torchvision.models import ResNet
from se_module import SELayer
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class SEBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None,
*, reduction=16):
super(SEBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, 1)
self.bn2 = nn.BatchNorm2d(planes)
self.se = SELayer(planes, reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class SEBottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None,
*, reduction=16):
super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(planes * 4, reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = 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)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
def se_resnet18(num_classes=1_000):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet34(num_classes=1_000):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet50(num_classes=1_000, pretrained=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
cur_path = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(cur_path, 'url.ini'), 'r') as f:
content = f.read()
seresnet50 = content.split('seresnet50=')[1].split('\n')[0]
model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
if pretrained:
model.load_state_dict(load_state_dict_from_url(seresnet50))
return model
def se_resnet101(num_classes=1_000):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet152(num_classes=1_000):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 8, 36, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
class CifarSEBasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, reduction=16):
super(CifarSEBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.se = SELayer(planes, reduction)
if inplanes != planes:
self.downsample = nn.Sequential(nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes))
else:
self.downsample = lambda x: x
self.stride = stride
def forward(self, x):
residual = self.downsample(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.se(out)
out += residual
out = self.relu(out)
return out
class CifarSEResNet(nn.Module):
def __init__(self, block, n_size, num_classes=10, reduction=16):
super(CifarSEResNet, self).__init__()
self.inplane = 16
self.conv1 = nn.Conv2d(
3, self.inplane, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplane)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(
block, 16, blocks=n_size, stride=1, reduction=reduction)
self.layer2 = self._make_layer(
block, 32, blocks=n_size, stride=2, reduction=reduction)
self.layer3 = self._make_layer(
block, 64, blocks=n_size, stride=2, reduction=reduction)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(64, num_classes)
self.initialize()
def initialize(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
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, reduction):
strides = [stride] + [1] * (blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.inplane, planes, stride, reduction))
self.inplane = planes
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
class CifarSEPreActResNet(CifarSEResNet):
def __init__(self, block, n_size, num_classes=10, reduction=16):
super(CifarSEPreActResNet, self).__init__(
block, n_size, num_classes, reduction)
self.bn1 = nn.BatchNorm2d(self.inplane)
self.initialize()
def forward(self, x):
x = self.conv1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.bn1(x)
x = self.relu(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
def se_resnet20(**kwargs):
"""Constructs a ResNet-18 model.
"""
model = CifarSEResNet(CifarSEBasicBlock, 3, **kwargs)
return model
def se_resnet32(**kwargs):
"""Constructs a ResNet-34 model.
"""
model = CifarSEResNet(CifarSEBasicBlock, 5, **kwargs)
return model
def se_resnet56(**kwargs):
"""Constructs a ResNet-34 model.
"""
model = CifarSEResNet(CifarSEBasicBlock, 9, **kwargs)
return model
def se_preactresnet20(**kwargs):
"""Constructs a ResNet-18 model.
"""
model = CifarSEPreActResNet(CifarSEBasicBlock, 3, **kwargs)
return model
def se_preactresnet32(**kwargs):
"""Constructs a ResNet-34 model.
"""
model = CifarSEPreActResNet(CifarSEBasicBlock, 5, **kwargs)
return model
def se_preactresnet56(**kwargs):
"""Constructs a ResNet-34 model.
"""
model = CifarSEPreActResNet(CifarSEBasicBlock, 9, **kwargs)
return model