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resnet.py
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resnet.py
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#Implementation taken from https://github.com/easezyc/deep-transfer-learning/blob/master/MUDA/MFSAN/MFSAN_2src/resnet.py
#Zhu Y, Zhuang F, Wang D. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 5989-5996.
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import loss
import torch.nn.functional as F
from torch.autograd import Variable
import torch
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.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)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, 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.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)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ADDneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(ADDneck, 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, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.stride = stride
def forward(self, 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.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.baselayer = [self.conv1, self.bn1, self.layer1, self.layer2, self.layer3, self.layer4]
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
#print(x.shape)
if x.size()[1] == 1:
x = x.repeat(1, 3, 1, 1)
#print(x.shape)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
class DAMS(nn.Module):
def __init__(self, num_classes=31):
super(DAMS, self).__init__()
self.sharedNet = resnet50(True)
self.sonnet1 = ADDneck(2048, 256)
self.sonnet2 = ADDneck(2048, 256)
self.cls_fc_son1 = nn.Linear(256, num_classes)
self.cls_fc_son2 = nn.Linear(256, num_classes)
self.avgpool = nn.AvgPool2d(7, stride=1)
def forward(self, data_src, data_tgt = 0, label_src = 0, mark = 1):
joint_loss = 0
if self.training == True:
if mark == 1:
data_src = self.sharedNet(data_src)
data_tgt = self.sharedNet(data_tgt)
data_tgt_son1 = self.sonnet1(data_tgt)
data_tgt_son1 = self.avgpool(data_tgt_son1)
data_tgt_son1 = data_tgt_son1.view(data_tgt_son1.size(0), -1)
data_src = self.sonnet1(data_src)
data_src = self.avgpool(data_src)
data_src = data_src.view(data_src.size(0), -1)
joint_loss += loss.mmd(data_src, data_tgt_son1)
joint_loss += loss.coral(data_src, data_tgt_son1)
data_tgt_son1 = self.cls_fc_son1(data_tgt_son1)
data_tgt_son2 = self.sonnet2(data_tgt)
data_tgt_son2 = self.avgpool(data_tgt_son2)
data_tgt_son2 = data_tgt_son2.view(data_tgt_son2.size(0), -1)
data_tgt_son2 = self.cls_fc_son2(data_tgt_son2)
l1_loss = torch.abs(torch.nn.functional.softmax(data_tgt_son1, dim=1) - torch.nn.functional.softmax(data_tgt_son2, dim=1))
l1_loss = torch.mean(l1_loss)
pred_src = self.cls_fc_son1(data_src)
cls_loss = F.nll_loss(F.log_softmax(pred_src, dim=1), label_src)
return cls_loss, joint_loss, l1_loss
if mark == 2:
data_src = self.sharedNet(data_src)
data_tgt = self.sharedNet(data_tgt)
data_tgt_son2 = self.sonnet2(data_tgt)
data_tgt_son2 = self.avgpool(data_tgt_son2)
data_tgt_son2 = data_tgt_son2.view(data_tgt_son2.size(0), -1)
data_src = self.sonnet2(data_src)
data_src = self.avgpool(data_src)
data_src = data_src.view(data_src.size(0), -1)
joint_loss += loss.mmd(data_src, data_tgt_son2)
joint_loss += loss.coral(data_src, data_tgt_son2)
data_tgt_son2 = self.cls_fc_son2(data_tgt_son2)
data_tgt_son1 = self.sonnet1(data_tgt)
data_tgt_son1 = self.avgpool(data_tgt_son1)
data_tgt_son1 = data_tgt_son1.view(data_tgt_son1.size(0), -1)
data_tgt_son1 = self.cls_fc_son1(data_tgt_son1)
l1_loss = torch.abs(torch.nn.functional.softmax(data_tgt_son1, dim=1) - torch.nn.functional.softmax(data_tgt_son2, dim=1))
l1_loss = torch.mean(l1_loss)
pred_src = self.cls_fc_son2(data_src)
cls_loss = F.nll_loss(F.log_softmax(pred_src, dim=1), label_src)
return cls_loss, joint_loss, l1_loss
else:
data = self.sharedNet(data_src)
fea_son1 = self.sonnet1(data)
fea_son1 = self.avgpool(fea_son1)
fea_son1 = fea_son1.view(fea_son1.size(0), -1)
pred1 = self.cls_fc_son1(fea_son1)
fea_son2 = self.sonnet2(data)
fea_son2 = self.avgpool(fea_son2)
fea_son2 = fea_son2.view(fea_son2.size(0), -1)
pred2 = self.cls_fc_son2(fea_son2)
return pred1, pred2
def resnet50(pretrained=False, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model