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reIDmodel.py
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"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import torch.nn as nn
from torch.nn import init
from torchvision import models
######################################################################
def weights_init_kaiming(m):
classname = m.__class__.__name__
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('InstanceNorm1d') != -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)
def fix_bn(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
m.eval()
# Defines the new fc layer and classification layer
# |--Linear--|--bn--|--relu--|--Linear--|
class ClassBlock(nn.Module):
def __init__(self, input_dim, class_num, droprate=0.5, relu=False, num_bottleneck=512):
super(ClassBlock, self).__init__()
add_block = []
add_block += [nn.Linear(input_dim, num_bottleneck)]
#num_bottleneck = input_dim # We remove the input_dim
add_block += [nn.BatchNorm1d(num_bottleneck, affine=True)]
if relu:
add_block += [nn.LeakyReLU(0.1)]
if droprate>0:
add_block += [nn.Dropout(p=droprate)]
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, norm=False, pool='avg', stride=2):
super(ft_net, self).__init__()
if norm:
self.norm = True
else:
self.norm = False
model_ft = models.resnet50(pretrained=True)
# avg pooling to global pooling
self.part = 4
if pool=='max':
model_ft.partpool = nn.AdaptiveMaxPool2d((self.part,1))
model_ft.avgpool = nn.AdaptiveMaxPool2d((1,1))
else:
model_ft.partpool = nn.AdaptiveAvgPool2d((self.part,1))
model_ft.avgpool = nn.AdaptiveAvgPool2d((1,1))
# remove the final downsample
if stride == 1:
model_ft.layer4[0].downsample[0].stride = (1,1)
model_ft.layer4[0].conv2.stride = (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) # -> 512 32*16
x = self.model.layer3(x)
x = self.model.layer4(x)
f = self.model.partpool(x) # 8 * 2048 4*1
x = self.model.avgpool(x) # 8 * 2048 1*1
x = x.view(x.size(0),x.size(1))
f = f.view(f.size(0),f.size(1)*self.part)
if self.norm:
fnorm = torch.norm(f, p=2, dim=1, keepdim=True) + 1e-8
f = f.div(fnorm.expand_as(f))
x = self.classifier(x)
return f, x
# Define the AB Model
#定义Ea，改造后的残差网络
"""
论文是这样描述Ea的:外观编码器）使用的是在ImageNet预训练的ResNet50模型，
并且移除了全局平均池化层和全连接层，然后添加了一个合适的自适应最大池化层去输出ap code （2048x4x1）,
然后通过两个全连接层， 被映射到primary feature f_prim和fine_grained feature f_fine（512 dim）
具体怎么卷积,怎么池化我就不介绍了，注意X开了两个分支，表示身份预测。
"""
class ft_netAB(nn.Module):
def __init__(self, class_num, norm=False, stride=2, droprate=0.5, pool='avg'):
super(ft_netAB, self).__init__()
model_ft = models.resnet50(pretrained=True)
self.part = 4
if pool=='max':
#自适应最大池化层
#partpool将输出重塑为（4,1）
#avgpool将输出重塑为（1,1）
model_ft.partpool = nn.AdaptiveMaxPool2d((self.part,1))
model_ft.avgpool = nn.AdaptiveMaxPool2d((1,1))
else:
#实际使用avg_pool？
model_ft.partpool = nn.AdaptiveAvgPool2d((self.part,1))
model_ft.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.model = model_ft
if stride == 1:
self.model.layer4[0].downsample[0].stride = (1,1)
self.model.layer4[0].conv2.stride = (1,1)
# 对身份进行预测
self.classifier1 = ClassBlock(2048, class_num, 0.5)
self.classifier2 = ClassBlock(2048, class_num, 0.75)
#x[batch,3,256,128]
def forward(self, x):
"""
下面这一段都是为了ap code，其中包含了
[身份信息]+ [衣服+鞋子+手机+包包等]，还没有进行分离
"""
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)
"""
# 这里进行分离，获得[衣服+鞋子+手机+包包等]
# f[batch_size, 2048, 4, 1]
"""
f = self.model.partpool(x)
# 相当于resize[batch_size, 2048, 4，1]
f = f.view(f.size(0),f.size(1)*self.part)
# 这个值后续不再计算梯度
f = f.detach() # no gradient
# 这里进行分离，分离出身份信息
# x[batch_size, 2048, 1, 1]
x = self.model.avgpool(x)
x = x.view(x.size(0), x.size(1))
# 身份信息又进行分离，分离出主要身份信息，和细致身份信息，
# 同时我们对身份的鉴别，也是这两个综合起来考虑的。
# x1[batch_size, class_num] = [batch_size, 751]
#L_prim
x1 = self.classifier1(x)
# x2[batch_size, class_num] = [batch_size, 751]
#L_fine
x2 = self.classifier2(x)
x=[]
x.append(x1)
x.append(x2)
return f, x
"""
re-id鉴别器是嵌入在生成模块中的，和编码器Ea是共用的。也就是说，编码器，不仅仅是编码器，其还是ReID行人从识别的模型（着重注意），代码注释也比较详细，就不讲解了。主要注意一个点，就这里进行了两次分离：
第一次分离：apcode 分离成 x[身份信息], f[衣服+鞋子+手机+包包等]信息的分离。
第二次分离：x[身份信息]分离成，主要身份信息，以及细致身份信息
至于他们分离的原理，当然是loss的定义了，后续有详细的分析。
"""
# 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 = 4 # We cut the pool5 to 4 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)
self.softmax = nn.Softmax(dim=1)
# define 4 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)
f = x
f = f.view(f.size(0),f.size(1)*self.part)
x = self.dropout(x)
part = {}
predict = {}
# get part feature batchsize*2048*4
for i in range(self.part):
part[i] = x[:,:,i].contiguous()
part[i] = part[i].view(x.size(0), x.size(1))
name = 'classifier'+str(i)
c = getattr(self,name)
predict[i] = c(part[i])
y=[]
for i in range(self.part):
y.append(predict[i])
return f, 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.layer3[0].downsample[0].stride = (1,1)
self.model.layer3[0].conv2.stride = (1,1)
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