搜索seo怎么优化日照seo优化

FaceNet的简介

Facenet的实现思路

import torch.nn as nndef 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 -> 80,80,32conv_bn(3, 32, 2), # 80,80,32 -> 80,80,64conv_dw(32, 64, 1), # 80,80,64 -> 40,40,128conv_dw(64, 128, 2),conv_dw(128, 128, 1),# 40,40,128 -> 20,20,256conv_dw(128, 256, 2),conv_dw(256, 256, 1),)self.stage2 = nn.Sequential(# 20,20,256 -> 10,10,512conv_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 -> 5,5,1024conv_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 = 1024elif backbone == "inception_resnetv1":self.backbone = inception_resnet()flat_shape = 1792else: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 xdef 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, xdef 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 = 1024elif backbone == "inception_resnetv1":self.backbone = inception_resnet()flat_shape = 1792else: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 xdef 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, xdef 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 = 1024elif backbone == "inception_resnetv1":self.backbone = inception_resnet()flat_shape = 1792else: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 xdef 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, xdef forward_classifier(self, x):x = self.classifier(x)return x

训练部分