class MyNet(nn.Module):
def __init__(self, pretrained=False, **kwargs):
super(MyNet, self).__init__()
netname = args.arch.split('_')[0]
if netname == 'resnet152':
self.model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
else:
self.model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
parameters = model_zoo.load_url(model_urls[netname])
self.model.load_state_dict(parameters)
self.model.avgpool = nn.AvgPool2d(8)
self.model.fc = nn.Linear(1024, 1)
self.model.sig = nn.Sigmoid()
def forward(self, x):
x = self.model.conv1(x)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.maxpool(x)
x = self.model.layer1(x)
x = self.model.layer2(x)
x = self.model.layer3(x)
# x = self.model.layer4(x)
x = self.model.avgpool(x)
x = x.view(x.size(0), -1)
x = self.model.fc(x)
return self.model.sig(x)
class OriNet(nn.Module):
def __init__(self,bits_len,pretrained=False,**kwargs):
super(OriNet,self).__init__()
self.bits_len=bits_len
self.resnetmodel=ResNet(BasicBlock,[2,2,2,2],**kwargs)
if pretrained:
parameters=model_zoo.load_url(model_urls['resnet18'])
self.model.load_state_dict(parameters)
self.resnetmodel.avgpool=None
self.resnetmodel.fc1=nn.Linear(512*3*4,512)
self.resnetmodel.fc2=nn.Linear(512,512)
self.resnetmodel.classifier=nn.Linear(512,bits_len)
self.register_buffer('centers',torch.zeros(bits_len,512))
def forward(self,input1):
input1=self.resnetmodel.conv1(input1)
input1=self.resnetmodel.bn1(input1)
input1=self.resnetmodel.relu(input1)
input1=self.resnetmodel.maxpool(input1)
input1=self.resnetmodel.layer1(input1)
input1=self.resnetmodel.layer2(input1)
input1=self.resnetmodel.layer3(input1)
input1=self.resnetmodel.layer4(input1)
input1=input1.view(input1.size(),-1)
input1=self.resnetmodel.fc1(input1)
#feature for center loss
input1=self.resnetmodel.fc2(input1)
self.features=input1
input1=self.resnetmodel.classifier(input1)
output1=F.log_softmax(input1)
return output1
class FaceModel(nn.Module):
def __init__(self, num_classes, pretrained=False, **kwargs):
super(FaceModel, self).__init__()
self.model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
parameters = model_zoo.load_url(model_urls['resnet18'])
self.model.load_state_dict(parameters)
self.model.avgpool = None
self.model.fc1 = nn.Linear(512 * 3 * 4, 512)
self.model.fc2 = nn.Linear(512, 512)
self.model.classifier = nn.Linear(512, num_classes)
self.register_buffer('centers', torch.zeros(num_classes, 512))
self.num_classes = num_classes
def forward(self, x):
x = self.model.conv1(x)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.maxpool(x)
x = self.model.layer1(x)
x = self.model.layer2(x)
x = self.model.layer3(x)
x = self.model.layer4(x)
x = x.view(x.size(0), -1)
x = self.model.fc1(x)
#feature for center loss
x = self.model.fc2(x)
self.features = x
x = self.model.classifier(x)
return F.log_softmax(x)
class ResNeXt50(BaseNetwork):
def __init__(self, opt):
super(ResNeXt50, self).__init__()
self.model = ResNet(Bottleneck, [3, 4, 6, 3],
groups=32,
width_per_group=4)
self.opt = opt
# self.reduced_id_dim = opt.reduced_id_dim
self.conv1x1 = nn.Conv2d(512 * Bottleneck.expansion,
512,
kernel_size=1,
padding=0)
self.fc = nn.Linear(512 * Bottleneck.expansion, opt.num_classes)
# self.fc_pre = nn.Sequential(nn.Linear(512 * Bottleneck.expansion, self.reduced_id_dim), nn.ReLU())
def load_pretrain(self):
check_point = torch.load(model_urls['resnext50_32x4d'])
util.copy_state_dict(check_point, self.model)
def forward_feature(self, input):
x = self.model.conv1(input)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.maxpool(x)
x = self.model.layer1(x)
x = self.model.layer2(x)
x = self.model.layer3(x)
x = self.model.layer4(x)
net = self.model.avgpool(x)
net = torch.flatten(net, 1)
x = self.conv1x1(x)
# x = self.fc_pre(x)
return net, x
def forward(self, input):
input_batch = input.view(-1, self.opt.output_nc, self.opt.crop_size,
self.opt.crop_size)
net, x = self.forward_feature(input_batch)
net = net.view(-1, self.opt.num_inputs, 512 * Bottleneck.expansion)
x = F.adaptive_avg_pool2d(x, (7, 7))
x = x.view(-1, self.opt.num_inputs, 512, 7, 7)
net = torch.mean(net, 1)
x = torch.mean(x, 1)
cls_scores = self.fc(net)
return [net, x], cls_scores
