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resnet_att_v3.py
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resnet_att_v3.py
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import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
__all__ = ['ResNet_att', 'resnet18_att', 'resnet34_att', 'resnet50_att', 'resnet101_att',
'resnet152_att']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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 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):
residual = 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:
residual = self.downsample(x)
out += residual
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 = 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 ResNet_att(nn.Module):
def __init__(self, block, layers, num_classes=3):
self.inplanes = 64
super(ResNet_att, 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) #nn.AdaptiveAvgPool2d(7)
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.block=block
self.T=1 #temperature for view pooling attention
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)
# self.V=nn.Sequential(
# nn.Linear(512 * block.expansion,1024),
# nn.ReLU(inplace=True),
# nn.Linear(1024,1),
# nn.CELU(inplace=True)
# )
self.att_conv=nn.Sequential(
nn.Conv2d(512*block.expansion,512,kernel_size=3,padding=0,bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512,256,kernel_size=3,padding=0,bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256,128,kernel_size=3,padding=0,bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True)
)
self.att_linear=nn.Sequential(nn.Linear(128,512))#,nn.CELU(inplace=True))
self.value=nn.Linear(512*block.expansion,512)
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):
# Swap batch and views dims
x = x.transpose(0, 1)
# print(x.shape)
# View pool
view_pool = []
weights=[]
for v in x:
v = self.conv1(v)
v = self.bn1(v)
v = self.relu(v)
v = self.maxpool(v)
v = self.layer1(v)
v = self.layer2(v)
v = self.layer3(v)
v = self.layer4(v)
w_conv=self.att_conv(v)
w_lin=self.att_linear(w_conv.view(v.size(0),-1))
v = self.avgpool(v)
v = v.view(v.size(0), -1)
v = self.value(v)
if self.training:
w=w_lin/self.T# +0.2*torch.rand(1).to(device)
else:
w=w_lin/self.T
view_pool.append(v)
weights.append(w)
weights=F.softmax(torch.stack(weights,2),2)
self.attention=weights
view_pool=torch.stack(view_pool,2)
pooled_view=(weights*view_pool).sum(2)
pooled_view = self.fc(pooled_view)
return pooled_view
def resnet18_att(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet_att(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet18'])
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
def resnet34_att(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet_att(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet34'])
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
def resnet50_att(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet_att(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet50'])
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
def resnet101_att(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet_att(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet101'])
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
def resnet152_att(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet_att(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet152'])
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model