人脸识别 FaceNet人脸识别（一种人脸识别与聚类的统一嵌入表示）

人脸识别 FaceNet人脸识别（一种人脸识别与聚类的统一嵌入表示）

FaceNet的简介Facenet的实现思路训练部分

FaceNet的简介

Facenet的实现思路

import torch.nn as nn

def conv_bn(inp, oup, stride = 1):

return nn.Sequential(

nn.Conv2d(inp, oup, 3, stride, 1, bias=False),

nn.BatchNorm2d(oup),

nn.ReLU6()

)

def conv_dw(inp, oup, stride = 1):

return nn.Sequential(

nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),

nn.BatchNorm2d(inp),

nn.ReLU6(),

nn.Conv2d(inp, oup, 1, 1, 0, bias=False),

nn.BatchNorm2d(oup),

nn.ReLU6(),

)

class MobileNetV1(nn.Module):

def __init__(self):

super(MobileNetV1, self).__init__()

self.stage1 = nn.Sequential(

# 160,160,3 -> 80,80,32

conv_bn(3, 32, 2),

# 80,80,32 -> 80,80,64

conv_dw(32, 64, 1),

# 80,80,64 -> 40,40,128

conv_dw(64, 128, 2),

conv_dw(128, 128, 1),

# 40,40,128 -> 20,20,256

conv_dw(128, 256, 2),

conv_dw(256, 256, 1),

)

self.stage2 = nn.Sequential(

# 20,20,256 -> 10,10,512

conv_dw(256, 512, 2),

conv_dw(512, 512, 1),

conv_dw(512, 512, 1),

conv_dw(512, 512, 1),

conv_dw(512, 512, 1),

conv_dw(512, 512, 1),

)

self.stage3 = nn.Sequential(

# 10,10,512 -> 5,5,1024

conv_dw(512, 1024, 2),

conv_dw(1024, 1024, 1),

)

self.avg = nn.AdaptiveAvgPool2d((1,1))

self.fc = nn.Linear(1024, 1000)

def forward(self, x):

x = self.stage1(x)

x = self.stage2(x)

x = self.stage3(x)

x = self.avg(x)

# x = self.model(x)

x = x.view(-1, 1024)

x = self.fc(x)

return x

class Facenet(nn.Module):

def __init__(self, backbone="mobilenet", dropout_keep_prob=0.5, embedding_size=128, num_classes=None, mode="train"):

super(Facenet, self).__init__()

if backbone == "mobilenet":

self.backbone = mobilenet()

flat_shape = 1024

elif backbone == "inception_resnetv1":

self.backbone = inception_resnet()

flat_shape = 1792

else:

raise ValueError('Unsupported backbone - `{}`, Use mobilenet, inception_resnetv1.'.format(backbone))

self.avg = nn.AdaptiveAvgPool2d((1,1))

self.Dropout = nn.Dropout(1 - dropout_keep_prob)

self.Bottleneck = nn.Linear(flat_shape, embedding_size,bias=False)

self.last_bn = nn.BatchNorm1d(embedding_size, eps=0.001, momentum=0.1, affine=True)

if mode == "train":

self.classifier = nn.Linear(embedding_size, num_classes)

def forward(self, x):

x = self.backbone(x)

x = self.avg(x)

x = x.view(x.size(0), -1)

x = self.Dropout(x)

x = self.Bottleneck(x)

x = self.last_bn(x)

x = F.normalize(x, p=2, dim=1)

return x

def forward_feature(self, x):

x = self.backbone(x)

x = self.avg(x)

x = x.view(x.size(0), -1)

x = self.Dropout(x)

x = self.Bottleneck(x)

before_normalize = self.last_bn(x)

x = F.normalize(before_normalize, p=2, dim=1)

return before_normalize, x

def forward_classifier(self, x):

x = self.classifier(x)

return x

在pytorch代码中，只需要一行就可以实现l2标准化的层。

class Facenet(nn.Module):

def __init__(self, backbone="mobilenet", dropout_keep_prob=0.5, embedding_size=128, num_classes=None, mode="train"):

super(Facenet, self).__init__()

if backbone == "mobilenet":

self.backbone = mobilenet()

flat_shape = 1024

elif backbone == "inception_resnetv1":

self.backbone = inception_resnet()

flat_shape = 1792

else:

raise ValueError('Unsupported backbone - `{}`, Use mobilenet, inception_resnetv1.'.format(backbone))

self.avg = nn.AdaptiveAvgPool2d((1,1))

self.Dropout = nn.Dropout(1 - dropout_keep_prob)

self.Bottleneck = nn.Linear(flat_shape, embedding_size,bias=False)

self.last_bn = nn.BatchNorm1d(embedding_size, eps=0.001, momentum=0.1, affine=True)

if mode == "train":

self.classifier = nn.Linear(embedding_size, num_classes)

def forward(self, x):

x = self.backbone(x)

x = self.avg(x)

x = x.view(x.size(0), -1)

x = self.Dropout(x)

x = self.Bottleneck(x)

x = self.last_bn(x)

x = F.normalize(x, p=2, dim=1)

return x

def forward_feature(self, x):

x = self.backbone(x)

x = self.avg(x)

x = x.view(x.size(0), -1)

x = self.Dropout(x)

x = self.Bottleneck(x)

before_normalize = self.last_bn(x)

x = F.normalize(before_normalize, p=2, dim=1)

return before_normalize, x

def forward_classifier(self, x):

x = self.classifier(x)

return x

class Facenet(nn.Module):

def __init__(self, backbone="mobilenet", dropout_keep_prob=0.5, embedding_size=128, num_classes=None, mode="train"):

super(Facenet, self).__init__()

if backbone == "mobilenet":

self.backbone = mobilenet()

flat_shape = 1024

elif backbone == "inception_resnetv1":

self.backbone = inception_resnet()

flat_shape = 1792

else:

raise ValueError('Unsupported backbone - `{}`, Use mobilenet, inception_resnetv1.'.format(backbone))

self.avg = nn.AdaptiveAvgPool2d((1,1))

self.Dropout = nn.Dropout(1 - dropout_keep_prob)

self.Bottleneck = nn.Linear(flat_shape, embedding_size,bias=False)

self.last_bn = nn.BatchNorm1d(embedding_size, eps=0.001, momentum=0.1, affine=True)

if mode == "train":

self.classifier = nn.Linear(embedding_size, num_classes)

def forward(self, x):

x = self.backbone(x)

x = self.avg(x)

x = x.view(x.size(0), -1)

x = self.Dropout(x)

x = self.Bottleneck(x)

x = self.last_bn(x)

x = F.normalize(x, p=2, dim=1)

return x

def forward_feature(self, x):

x = self.backbone(x)

x = self.avg(x)

x = x.view(x.size(0), -1)

x = self.Dropout(x)

x = self.Bottleneck(x)

before_normalize = self.last_bn(x)

x = F.normalize(before_normalize, p=2, dim=1)

return before_normalize, x

def forward_classifier(self, x):

x = self.classifier(x)

return x

训练部分

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