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sresnet.py
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sresnet.py
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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': './pretrain/resnet18-5c106cde.pth',
'resnet34': './pretrain/resnet34-333f7ec4.pth',
'resnet50': './pretrain/resnet50-19c8e357.pth',
'resnet101': './pretrain/resnet101-5d3b4d8f.pth',
'resnet152': './pretrain/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, 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.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)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
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
def ScalaNet(channel_in, channel_out, size):
return nn.Sequential(
nn.Conv2d(channel_in, 128, kernel_size=1, stride=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=size, stride=size),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, channel_out, kernel_size=1, stride=1),
nn.BatchNorm2d(channel_out),
nn.ReLU(),
nn.AvgPool2d(4, 4)
)
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=100, zero_init_residual=False, align="CONV"):
super(ResNet, self).__init__()
print("num_class: ", num_classes)
self.inplanes = 64
self.align = align
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1,
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)
print("CONV for aligning")
self.scala1 = ScalaNet(
channel_in=64*block.expansion,
channel_out=512*block.expansion,
size=8
)
self.scala2 = ScalaNet(
channel_in=128 * block.expansion,
channel_out=512 * block.expansion,
size=4
)
self.scala3 = ScalaNet(
channel_in=256 * block.expansion,
channel_out=512 * block.expansion,
size=2
)
self.scala4 = nn.AvgPool2d(4, 4)
self.attention1 = nn.Sequential(
nn.Conv2d(kernel_size=3, padding=1, stride=2, in_channels=64* block.expansion, out_channels=64* block.expansion),
nn.BatchNorm2d(64* block.expansion),
nn.ReLU(),
nn.ConvTranspose2d(kernel_size=4, padding=1, stride=2, in_channels=64* block.expansion, out_channels=64* block.expansion),
nn.BatchNorm2d(64* block.expansion),
nn.Sigmoid()
)
self.attention2 = nn.Sequential(
nn.Conv2d(kernel_size=3, padding=1, stride=2, in_channels=128* block.expansion, out_channels=128* block.expansion),
nn.BatchNorm2d(128* block.expansion),
nn.ReLU(),
nn.ConvTranspose2d(kernel_size=4, padding=1, stride=2, in_channels=128* block.expansion, out_channels=128* block.expansion),
nn.BatchNorm2d(128* block.expansion),
nn.Sigmoid()
)
self.attention3 = nn.Sequential(
nn.Conv2d(kernel_size=3, padding=1, stride=2, in_channels=256* block.expansion, out_channels=256* block.expansion),
nn.BatchNorm2d(256* block.expansion),
nn.ReLU(),
nn.ConvTranspose2d(kernel_size=4, padding=1, stride=2, in_channels=256* block.expansion, out_channels=256* block.expansion),
nn.BatchNorm2d(256* block.expansion),
nn.Sigmoid()
)
self.fc1 = nn.Linear(512 * block.expansion, num_classes)
self.fc2 = nn.Linear(512 * block.expansion, num_classes)
self.fc3 = nn.Linear(512 * block.expansion, num_classes)
self.fc4 = nn.Linear(512 * block.expansion, num_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)
# 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)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.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 forward(self, x):
feature_list = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
fea1 = self.attention1(x)
fea1 = fea1 * x
feature_list.append(fea1)
x = self.layer2(x)
fea2 = self.attention2(x)
fea2 = fea2 * x
feature_list.append(fea2)
x = self.layer3(x)
fea3 = self.attention3(x)
fea3 = fea3 * x
feature_list.append(fea3)
x = self.layer4(x)
feature_list.append(x)
out1_feature = self.scala1(feature_list[0]).view(x.size(0), -1)
out2_feature = self.scala2(feature_list[1]).view(x.size(0), -1)
out3_feature = self.scala3(feature_list[2]).view(x.size(0), -1)
out4_feature = self.scala4(feature_list[3]).view(x.size(0), -1)
teacher_feature = out4_feature.detach()
feature_loss = ((teacher_feature - out3_feature)**2 + (teacher_feature - out2_feature)**2 +\
(teacher_feature - out1_feature)**2).sum()
out1 = self.fc1(out1_feature)
out2 = self.fc2(out2_feature)
out3 = self.fc3(out3_feature)
out4 = self.fc4(out4_feature)
return [out4, out3, out2, out1], feature_loss
# None is prepared for Hint Learning
def resnet18(pretrained=False, **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:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, **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:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model