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multi_channel_resnet34_hyper.py
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multi_channel_resnet34_hyper.py
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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
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',
}
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 * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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(nn.Module):
def __init__(self, block, layers):
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.insert1 = nn.Conv2d(64,64,kernel_size=3,stride=2, padding=1,bias=False)
self.insert2 = nn.Conv2d(64,128,kernel_size=1,stride=1, padding=0,bias=False)
self.insert3 = nn.Conv2d(128,128,kernel_size=3,stride=2, padding=1,bias=False)
self.insert4 = nn.Conv2d(128,256,kernel_size=1,stride=1, padding=0,bias=False)
self.insert5 = nn.Conv2d(256,256,kernel_size=3,stride=2, padding=1,bias=False)
self.insert6 = nn.Conv2d(256,512,kernel_size=1,stride=1, padding=0,bias=False)
self.spatial_weights1 = nn.Conv2d(512,256,kernel_size=1,stride=1,padding=0,bias=False)
self.spatial_weights2 = nn.Conv2d(256,128,kernel_size=1,stride=1,padding=0,bias=False)
self.spatial_weights3 = nn.Conv2d(128,1,kernel_size=1,stride=1,padding=0,bias=False)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.feature_embedding = nn.Linear(512 * block.expansion, 10)
self.quality = nn.Linear(10*6, 1)
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)
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_impl(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x_insert = self.insert1(x)
x_insert = self.insert2(x_insert)
x = self.layer2(x)
x_insert = self.insert3(x_insert+x)
x_insert = self.insert4(x_insert)
x = self.layer3(x)
x_insert = self.insert5(x_insert+x)
x_insert = self.insert6(x_insert)
x = self.layer4(x)
x = x + x_insert
x = self.relu(x)
x_spatial = self.spatial_weights1(x)
x_spatial = self.relu(x_spatial)
x_spatial = self.spatial_weights2(x_spatial)
x_spatial = self.relu(x_spatial)
x_spatial = self.spatial_weights3(x_spatial)
x = torch.mul(x, x_spatial)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.feature_embedding(x)
return x
def forward(self, x_BA, x_BO, x_F, x_L, x_R, x_T):
# image BA
x_BA = self._forward_impl(x_BA)
x_BO = self._forward_impl(x_BO)
x_F = self._forward_impl(x_F)
x_L = self._forward_impl(x_L)
x_R = self._forward_impl(x_R)
x_T = self._forward_impl(x_T)
x = torch.cat([x_BA,x_BO,x_F,x_L,x_R,x_T],1)
x = self.quality(x)
return x
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']))
model_dict = model.state_dict()
pre_train_model = model_zoo.load_url(model_urls['resnet18'])
pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
model_dict.update(pre_train_model)
model.load_state_dict(model_dict)
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_dict = model.state_dict()
pre_train_model = model_zoo.load_url(model_urls['resnet34'])
pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
model_dict.update(pre_train_model)
model.load_state_dict(model_dict)
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']))
model_dict = model.state_dict()
pre_train_model = model_zoo.load_url(model_urls['resnet50'])
pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
model_dict.update(pre_train_model)
model.load_state_dict(model_dict)
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