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model.py
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model.py
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import torch
import torch.nn as nn
from torch.nn import init
from torchvision import models
from torch.autograd import Variable
import numpy as np
import LOMO
import decent_dim
######################################################################
def weights_init_kaiming(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_out')
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm1d') != -1:
init.normal_(m.weight.data, 1.0, 0.02)
init.constant_(m.bias.data, 0.0)
def weights_init_classifier(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
init.normal_(m.weight.data, std=0.001)
init.constant_(m.bias.data, 0.0)
# Defines the new fc layer and classification layer
# |--Linear--|--bn--|--relu--|--Linear--|
class ClassBlock(nn.Module):
def __init__(self, input_dim, class_num, dropout=True, relu=True, num_bottleneck=512):
super(ClassBlock, self).__init__()
add_block = []
add_block += [nn.Linear(input_dim, num_bottleneck)]
add_block += [nn.BatchNorm1d(num_bottleneck)]
if relu:
add_block += [nn.LeakyReLU(0.1)]
if dropout:
add_block += [nn.Dropout(p=0.5)]
add_block = nn.Sequential(*add_block)
add_block.apply(weights_init_kaiming)
classifier = []
classifier += [nn.Linear(num_bottleneck, class_num)]
classifier = nn.Sequential(*classifier)
classifier.apply(weights_init_classifier)
self.add_block = add_block
self.classifier = classifier
def forward(self, x):
x = self.add_block(x)
x = self.classifier(x)
return x
# Define the ResNet50-based Model
class ft_net(nn.Module):
def __init__(self, class_num ):
super(ft_net, self).__init__()
model_ft = models.resnet50(pretrained=True)
# avg pooling to global pooling
model_ft.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.model = model_ft
self.classifier = ClassBlock(2048, class_num)
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 = torch.squeeze(x)
x = self.classifier(x)
return x
# Define the DenseNet121-based Model
class ft_net_dense(nn.Module):
def __init__(self, class_num ):
super().__init__()
model_ft = models.densenet121(pretrained=True)
model_ft.features.avgpool = nn.AdaptiveAvgPool2d((1,1))
model_ft.fc = nn.Sequential()
self.model = model_ft
# For DenseNet, the feature dim is 1024
self.classifier = ClassBlock(1024, class_num)
def forward(self, x):
x = self.model.features(x)
x = torch.squeeze(x)
x = self.classifier(x)
return x
# Define the ResNet50-based Model (Middle-Concat)
# In the spirit of "The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching." Yu, Qian, et al. arXiv:1711.08106 (2017).
class ft_net_middle(nn.Module):
def __init__(self, class_num ):
super(ft_net_middle, self).__init__()
model_ft = models.resnet50(pretrained=True)
# avg pooling to global pooling
model_ft.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.model = model_ft
self.classifier = ClassBlock(2048+1024, class_num)
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)
# x0 n*1024*1*1
x0 = self.model.avgpool(x)
x = self.model.layer4(x)
# x1 n*2048*1*1
x1 = self.model.avgpool(x)
x = torch.cat((x0,x1),1)
x = torch.squeeze(x)
x = self.classifier(x)
return x
# Part Model proposed in Yifan Sun etal. (2018)
class PCB(nn.Module):
def __init__(self, class_num ):
super(PCB, self).__init__()
self.part = 6 # We cut the pool5 to 6 parts
model_ft = models.resnet50(pretrained=True)
self.model = model_ft
self.avgpool = nn.AdaptiveAvgPool2d((self.part,1))
self.dropout = nn.Dropout(p=0.5)
# remove the final downsample
self.model.layer4[0].downsample[0].stride = (1,1)
self.model.layer4[0].conv2.stride = (1,1)
# define 6 classifiers
for i in range(self.part):
name = 'classifier'+str(i)
setattr(self, name, ClassBlock(2048, class_num, True, False, 256))
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.avgpool(x)
x = self.dropout(x)
part = {}
predict = {}
# get six part feature batchsize*2048*6
for i in range(self.part):
part[i] = torch.squeeze(x[:,:,i])
name = 'classifier'+str(i)
c = getattr(self,name)
predict[i] = c(part[i])
# sum prediction
#y = predict[0]
#for i in range(self.part-1):
# y += predict[i+1]
y = []
for i in range(self.part):
y.append(predict[i])
return y
class PCB_test(nn.Module):
def __init__(self,model):
super(PCB_test,self).__init__()
self.part = 6
self.model = model.model
self.avgpool = nn.AdaptiveAvgPool2d((self.part,1))
# remove the final downsample
self.model.layer4[0].downsample[0].stride = (1,1)
self.model.layer4[0].conv2.stride = (1,1)
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.avgpool(x)
y = x.view(x.size(0),x.size(1),x.size(2))
return y
class fusion_net(nn.Module):
def __init__(self, class_num ):
super(fusion_net, self).__init__()
model_ft = models.resnet50(pretrained=True)
# avg pooling to global pooling
model_ft.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.model = model_ft
#2048+4096=6144
self.classifier = ClassBlock(4070, class_num)
#self.buffer1=nn.Linear(4096,4096,bias=1)
<<<<<<< HEAD
#hand_feature=np.ones((2,2022))
#self.hand_feature=Variable(torch.from_numpy(hand_feature).cuda().float(),requires_grad=True)
self.liangzi=0
=======
>>>>>>> cfa8d3b0456de987f25a1411cdb245bc11db8de5
def forward(self, x):
#传统特征
hand_feature = decent_dim.dd(LOMO.LOMO(x.cpu().numpy()),dAfter=2022)
#深度特征
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)
<<<<<<< HEAD
#x = torch.squeeze(x)
x=x.reshape(2,-1)
#print('***************************************************************{}'.format(self.liangzi))
#self.liangzi+=1
=======
x = torch.squeeze(x)
#print('***************************************************************')
>>>>>>> cfa8d3b0456de987f25a1411cdb245bc11db8de5
#print(x.shape)
#print(type(x))
#融合
#print(hand_feature.shape)
x=torch.cat((x,hand_feature),1)
x=torch.squeeze(x)
#print(x.shape)
#x=x.reshape(1,-1)
x = self.classifier(x)
return x
# debug model structure
#net = ft_net(751)
net = ft_net_dense(751)
#print(net)
input = Variable(torch.FloatTensor(8, 3, 224, 224))
output = net(input)
print('net output size:')
print(output.shape)