参考：

https://github.com/ronghuaiyang/arcface-pytorch

1.代码结构

目录结构如下：

配置信息：包含许多训练或者测试的一些信息配置，比如backbone选用的模型结构等；

数据读取：里面是一个pytorch的Dataloder，我们可以自定义，其中__getitem__用于迭代时输出图像与label数据对；

模型：这里面含有backbone结构，loss损失函数等结构；

脚本：这里面放了一些其他的用于后续处理的脚本。

test：测试脚本

train：训练脚本

2.关键脚本解析

2.1 config/config.py文件

这里是使用一个类设置初始变量配置信息。在调用的使用只需要实例化这个类，然后用这个对象名称加上.env（里面的参数名，就可以调用了）

class Config(object):env = 'default'backbone = 'resnet18'classify = 'softmax'num_classes = 13938metric = 'arc_margin'easy_margin = Falseuse_se = Falseloss = 'focal_loss'display = Falsefinetune = Falsetrain_root = '/data/Datasets/webface/CASIA-maxpy-clean-crop-144/'train_list = '/data/Datasets/webface/train_data_13938.txt'val_list = '/data/Datasets/webface/val_data_13938.txt'test_root = '/data1/Datasets/anti-spoofing/test/data_align_256'test_list = 'test.txt'lfw_root = '/data/Datasets/lfw/lfw-align-128'lfw_test_list = '/data/Datasets/lfw/lfw_test_pair.txt'checkpoints_path = 'checkpoints'load_model_path = 'models/resnet18.pth'test_model_path = 'checkpoints/resnet18_110.pth'save_interval = 10train_batch_size = 16  # batch sizetest_batch_size = 60input_shape = (1, 128, 128)optimizer = 'sgd'use_gpu = True  # use GPU or notgpu_id = '0, 1'num_workers = 4  # how many workers for loading dataprint_freq = 100  # print info every N batchdebug_file = '/tmp/debug'  # if os.path.exists(debug_file): enter ipdbresult_file = 'result.csv'max_epoch = 50lr = 1e-1  # initial learning ratelr_step = 10lr_decay = 0.95  # when val_loss increase, lr = lr*lr_decayweight_decay = 5e-4

2.2 data/dataset.py文件

参考此处：【从DataLoader到Model（一）】三步法写自定义Torch的DataLoader - 知乎

我们会发现下面的dataloader是一个标准的dataloader格式，包含__init__,__getitem__,__len__，这几个方法。其中__init__中定义了图像从文件目录中读取到一个list中，以及train模式下与其他模式下要进行的一些tranforms的一些数据增强操作。__getitem__会读取图像，并进行初始化过程中的一些transforms操作，并返回图像与labels。__len__返回图像的总数目。通过main函数中，前面几行就可以按批次迭代地调用数据了。

import os
from PIL import Image
import torch
from torch.utils import data
import numpy as np
from torchvision import transforms as T
import torchvision
import cv2
import sysclass Dataset(data.Dataset):def __init__(self, root, data_list_file, phase='train', input_shape=(1, 128, 128)):self.phase = phaseself.input_shape = input_shapewith open(os.path.join(data_list_file), 'r') as fd:imgs = fd.readlines()imgs = [os.path.join(root, img[:-1]) for img in imgs]self.imgs = np.random.permutation(imgs)# normalize = T.Normalize(mean=[0.5, 0.5, 0.5],#                         std=[0.5, 0.5, 0.5])normalize = T.Normalize(mean=[0.5], std=[0.5])if self.phase == 'train':self.transforms = T.Compose([T.RandomCrop(self.input_shape[1:]),T.RandomHorizontalFlip(),T.ToTensor(),normalize])else:self.transforms = T.Compose([T.CenterCrop(self.input_shape[1:]),T.ToTensor(),normalize])def __getitem__(self, index):sample = self.imgs[index]splits = sample.split()img_path = splits[0]data = Image.open(img_path)data = data.convert('L')data = self.transforms(data)label = np.int32(splits[1])return data.float(), labeldef __len__(self):return len(self.imgs)if __name__ == '__main__':dataset = Dataset(root='/data/Datasets/fv/dataset_v1.1/dataset_mix_aligned_v1.1',data_list_file='/data/Datasets/fv/dataset_v1.1/mix_20w.txt',phase='test',input_shape=(1, 128, 128))trainloader = data.DataLoader(dataset, batch_size=10)for i, (data, label) in enumerate(trainloader):# imgs, labels = data# print imgs.numpy().shape# print data.cpu().numpy()# if i == 0:img = torchvision.utils.make_grid(data).numpy()# print img.shape# print label.shape# chw -> hwcimg = np.transpose(img, (1, 2, 0))# img *= np.array([0.229, 0.224, 0.225])# img += np.array([0.485, 0.456, 0.406])img += np.array([1, 1, 1])img *= 127.5img = img.astype(np.uint8)img = img[:, :, [2, 1, 0]]cv2.imshow('img', img)cv2.waitKey()# break# dst.decode_segmap(labels.numpy()[0], plot=True)

2.3 models/resnet.py

下面的结构应该是在resnet基础上修改的，包含resnetface。

# -*- coding: utf-8 -*-
"""
Created on 18-5-21 下午5:26@author: ronghuaiyang
"""
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.utils.weight_norm as weight_norm
import torch.nn.functional as F# __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
#            'resnet152']model_urls = {'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth','resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth','resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth','resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth','resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}def conv3x3(in_planes, out_planes, stride=1):"""3x3 convolution with padding"""return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False)class BasicBlock(nn.Module):expansion = 1def __init__(self, inplanes, planes, stride=1, downsample=None):super(BasicBlock, self).__init__()self.conv1 = conv3x3(inplanes, planes, stride)self.bn1 = nn.BatchNorm2d(planes)self.relu = nn.ReLU(inplace=True)self.conv2 = conv3x3(planes, planes)self.bn2 = nn.BatchNorm2d(planes)self.downsample = downsampleself.stride = stridedef forward(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)if self.downsample is not None:residual = self.downsample(x)out += residualout = self.relu(out)return outclass IRBlock(nn.Module):expansion = 1def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):super(IRBlock, self).__init__()self.bn0 = nn.BatchNorm2d(inplanes)self.conv1 = conv3x3(inplanes, inplanes)self.bn1 = nn.BatchNorm2d(inplanes)self.prelu = nn.PReLU()self.conv2 = conv3x3(inplanes, planes, stride)self.bn2 = nn.BatchNorm2d(planes)self.downsample = downsampleself.stride = strideself.use_se = use_seif self.use_se:self.se = SEBlock(planes)def forward(self, x):residual = xout = self.bn0(x)out = self.conv1(out)out = self.bn1(out)out = self.prelu(out)out = self.conv2(out)out = self.bn2(out)if self.use_se:out = self.se(out)if self.downsample is not None:residual = self.downsample(x)out += residualout = self.prelu(out)return outclass Bottleneck(nn.Module):expansion = 4def __init__(self, inplanes, planes, stride=1, downsample=None):super(Bottleneck, self).__init__()self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)self.bn1 = nn.BatchNorm2d(planes)self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,padding=1, bias=False)self.bn2 = nn.BatchNorm2d(planes)self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)self.bn3 = nn.BatchNorm2d(planes * self.expansion)self.relu = nn.ReLU(inplace=True)self.downsample = downsampleself.stride = stridedef forward(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)if self.downsample is not None:residual = self.downsample(x)out += residualout = self.relu(out)return outclass SEBlock(nn.Module):def __init__(self, channel, reduction=16):super(SEBlock, self).__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.fc = nn.Sequential(nn.Linear(channel, channel // reduction),nn.PReLU(),nn.Linear(channel // reduction, channel),nn.Sigmoid())def forward(self, x):b, c, _, _ = x.size()y = self.avg_pool(x).view(b, c)y = self.fc(y).view(b, c, 1, 1)return x * yclass ResNetFace(nn.Module):def __init__(self, block, layers, use_se=True):self.inplanes = 64self.use_se = use_sesuper(ResNetFace, self).__init__()self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)self.bn1 = nn.BatchNorm2d(64)self.prelu = nn.PReLU()self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)self.layer1 = self._make_layer(block, 64, layers[0])self.layer2 = self._make_layer(block, 128, layers[1], stride=2)self.layer3 = self._make_layer(block, 256, layers[2], stride=2)self.layer4 = self._make_layer(block, 512, layers[3], stride=2)self.bn4 = nn.BatchNorm2d(512)self.dropout = nn.Dropout()self.fc5 = nn.Linear(512 * 8 * 8, 512)self.bn5 = nn.BatchNorm1d(512)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.xavier_normal_(m.weight)elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.xavier_normal_(m.weight)nn.init.constant_(m.bias, 0)def _make_layer(self, block, planes, blocks, stride=1):downsample = Noneif stride != 1 or self.inplanes != planes * block.expansion:downsample = nn.Sequential(nn.Conv2d(self.inplanes, planes * block.expansion,kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(planes * block.expansion),)layers = []layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))self.inplanes = planesfor i in range(1, blocks):layers.append(block(self.inplanes, planes, use_se=self.use_se))return nn.Sequential(*layers)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.prelu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.bn4(x)x = self.dropout(x)x = x.view(x.size(0), -1)x = self.fc5(x)x = self.bn5(x)return xclass ResNet(nn.Module):def __init__(self, block, layers):self.inplanes = 64super(ResNet, self).__init__()# self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,#                        bias=False)self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1,bias=False)self.bn1 = nn.BatchNorm2d(64)self.relu = nn.ReLU(inplace=True)# self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = self._make_layer(block, 64, layers[0], stride=2)self.layer2 = self._make_layer(block, 128, layers[1], stride=2)self.layer3 = self._make_layer(block, 256, layers[2], stride=2)self.layer4 = self._make_layer(block, 512, layers[3], stride=2)# self.avgpool = nn.AvgPool2d(8, stride=1)# self.fc = nn.Linear(512 * block.expansion, num_classes)self.fc5 = nn.Linear(512 * 8 * 8, 512)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)def _make_layer(self, block, planes, blocks, stride=1):downsample = Noneif stride != 1 or self.inplanes != planes * block.expansion:downsample = nn.Sequential(nn.Conv2d(self.inplanes, planes * block.expansion,kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(planes * block.expansion),)layers = []layers.append(block(self.inplanes, planes, stride, downsample))self.inplanes = planes * block.expansionfor i in range(1, blocks):layers.append(block(self.inplanes, planes))return nn.Sequential(*layers)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)# x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)# x = nn.AvgPool2d(kernel_size=x.size()[2:])(x)# x = self.avgpool(x)x = x.view(x.size(0), -1)x = self.fc5(x)return xdef resnet18(pretrained=False, **kwargs):"""Constructs a ResNet-18 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))return modeldef resnet34(pretrained=False, **kwargs):"""Constructs a ResNet-34 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))return modeldef resnet50(pretrained=False, **kwargs):"""Constructs a ResNet-50 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))return modeldef resnet101(pretrained=False, **kwargs):"""Constructs a ResNet-101 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))return modeldef resnet152(pretrained=False, **kwargs):"""Constructs a ResNet-152 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))return modeldef resnet_face18(use_se=True, **kwargs):model = ResNetFace(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs)return model

2.4 models/metrics.py

这里应该是度量学习的一些指标，最后一层的特征表示成什么样，具体后续补充。

from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import mathclass ArcMarginProduct(nn.Module):r"""Implement of large margin arc distance: :Args:in_features: size of each input sampleout_features: size of each output samples: norm of input featurem: margincos(theta + m)"""def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):super(ArcMarginProduct, self).__init__()self.in_features = in_featuresself.out_features = out_featuresself.s = sself.m = mself.weight = Parameter(torch.FloatTensor(out_features, in_features))nn.init.xavier_uniform_(self.weight)self.easy_margin = easy_marginself.cos_m = math.cos(m)self.sin_m = math.sin(m)self.th = math.cos(math.pi - m)self.mm = math.sin(math.pi - m) * mdef forward(self, input, label):# --------------------------- cos(theta) & phi(theta) ---------------------------cosine = F.linear(F.normalize(input), F.normalize(self.weight))sine = torch.sqrt((1.0 - torch.pow(cosine, 2)).clamp(0, 1))phi = cosine * self.cos_m - sine * self.sin_mif self.easy_margin:phi = torch.where(cosine > 0, phi, cosine)else:phi = torch.where(cosine > self.th, phi, cosine - self.mm)# --------------------------- convert label to one-hot ---------------------------# one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')one_hot = torch.zeros(cosine.size(), device='cuda')one_hot.scatter_(1, label.view(-1, 1).long(), 1)# -------------torch.where(out_i = {x_i if condition_i else y_i) -------------output = (one_hot * phi) + ((1.0 - one_hot) * cosine)  # you can use torch.where if your torch.__version__ is 0.4output *= self.s# print(output)return outputclass AddMarginProduct(nn.Module):r"""Implement of large margin cosine distance: :Args:in_features: size of each input sampleout_features: size of each output samples: norm of input featurem: margincos(theta) - m"""def __init__(self, in_features, out_features, s=30.0, m=0.40):super(AddMarginProduct, self).__init__()self.in_features = in_featuresself.out_features = out_featuresself.s = sself.m = mself.weight = Parameter(torch.FloatTensor(out_features, in_features))nn.init.xavier_uniform_(self.weight)def forward(self, input, label):# --------------------------- cos(theta) & phi(theta) ---------------------------cosine = F.linear(F.normalize(input), F.normalize(self.weight))phi = cosine - self.m# --------------------------- convert label to one-hot ---------------------------one_hot = torch.zeros(cosine.size(), device='cuda')# one_hot = one_hot.cuda() if cosine.is_cuda else one_hotone_hot.scatter_(1, label.view(-1, 1).long(), 1)# -------------torch.where(out_i = {x_i if condition_i else y_i) -------------output = (one_hot * phi) + ((1.0 - one_hot) * cosine)  # you can use torch.where if your torch.__version__ is 0.4output *= self.s# print(output)return outputdef __repr__(self):return self.__class__.__name__ + '(' \+ 'in_features=' + str(self.in_features) \+ ', out_features=' + str(self.out_features) \+ ', s=' + str(self.s) \+ ', m=' + str(self.m) + ')'class SphereProduct(nn.Module):r"""Implement of large margin cosine distance: :Args:in_features: size of each input sampleout_features: size of each output samplem: margincos(m*theta)"""def __init__(self, in_features, out_features, m=4):super(SphereProduct, self).__init__()self.in_features = in_featuresself.out_features = out_featuresself.m = mself.base = 1000.0self.gamma = 0.12self.power = 1self.LambdaMin = 5.0self.iter = 0self.weight = Parameter(torch.FloatTensor(out_features, in_features))nn.init.xavier_uniform(self.weight)# duplication formulaself.mlambda = [lambda x: x ** 0,lambda x: x ** 1,lambda x: 2 * x ** 2 - 1,lambda x: 4 * x ** 3 - 3 * x,lambda x: 8 * x ** 4 - 8 * x ** 2 + 1,lambda x: 16 * x ** 5 - 20 * x ** 3 + 5 * x]def forward(self, input, label):# lambda = max(lambda_min,base*(1+gamma*iteration)^(-power))self.iter += 1self.lamb = max(self.LambdaMin, self.base * (1 + self.gamma * self.iter) ** (-1 * self.power))# --------------------------- cos(theta) & phi(theta) ---------------------------cos_theta = F.linear(F.normalize(input), F.normalize(self.weight))cos_theta = cos_theta.clamp(-1, 1)cos_m_theta = self.mlambda[self.m](cos_theta)theta = cos_theta.data.acos()k = (self.m * theta / 3.14159265).floor()phi_theta = ((-1.0) ** k) * cos_m_theta - 2 * kNormOfFeature = torch.norm(input, 2, 1)# --------------------------- convert label to one-hot ---------------------------one_hot = torch.zeros(cos_theta.size())one_hot = one_hot.cuda() if cos_theta.is_cuda else one_hotone_hot.scatter_(1, label.view(-1, 1), 1)# --------------------------- Calculate output ---------------------------output = (one_hot * (phi_theta - cos_theta) / (1 + self.lamb)) + cos_thetaoutput *= NormOfFeature.view(-1, 1)return outputdef __repr__(self):return self.__class__.__name__ + '(' \+ 'in_features=' + str(self.in_features) \+ ', out_features=' + str(self.out_features) \+ ', m=' + str(self.m) + ')'

2.5 utils/view_model.py

大概是输出模型每层的参数量的一个函数。

import torch
from torch.autograd import Variable
import torch.nn as nn
from graphviz import Digraph__all__ = ['view_model']class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2),nn.ReLU(),nn.MaxPool2d(kernel_size=2))self.conv2 = nn.Sequential(nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),nn.ReLU(),nn.MaxPool2d(kernel_size=2))self.out = nn.Linear(32*7*7, 10)def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = x.view(x.size(0), -1)  # (batch, 32*7*7)out = self.out(x)return outdef make_dot(var, params=None):""" Produces Graphviz representation of PyTorch autograd graphBlue nodes are the Variables that require grad, orange are Tensorssaved for backward in torch.autograd.FunctionArgs:var: output Variableparams: dict of (name, Variable) to add names to node thatrequire grad (TODO: make optional)"""if params is not None:assert isinstance(params.values()[0], Variable)param_map = {id(v): k for k, v in params.items()}node_attr = dict(style='filled',shape='box',align='left',fontsize='12',ranksep='0.1',height='0.2')dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))seen = set()def size_to_str(size):return '('+(', ').join(['%d' % v for v in size])+')'def add_nodes(var):if var not in seen:if torch.is_tensor(var):dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')elif hasattr(var, 'variable'):u = var.variablename = param_map[id(u)] if params is not None else ''node_name = '%s\n %s' % (name, size_to_str(u.size()))dot.node(str(id(var)), node_name, fillcolor='lightblue')else:dot.node(str(id(var)), str(type(var).__name__))seen.add(var)if hasattr(var, 'next_functions'):for u in var.next_functions:if u[0] is not None:dot.edge(str(id(u[0])), str(id(var)))add_nodes(u[0])if hasattr(var, 'saved_tensors'):for t in var.saved_tensors:dot.edge(str(id(t)), str(id(var)))add_nodes(t)add_nodes(var.grad_fn)return dotdef view_model(net, input_shape):x = Variable(torch.randn(1, *input_shape))y = net(x)g = make_dot(y)g.view()params = list(net.parameters())k = 0for i in params:l = 1print("layer parameters size:" + str(list(i.size())))for j in i.size():l *= jprint("layer parameters:" + str(l))k = k + lprint("total parameters:" + str(k))if __name__ == '__main__':net = CNN()view_model(net)# x = Variable(torch.randn(1, 1, 28, 28))# y = net(x)# g = make_dot(y)# g.view()## params = list(net.parameters())# k = 0# for i in params:#     l = 1#     print("layer parameters:" + str(list(i.size())))#     for j in i.size():#         l *= j#     print("layer parameters:" + str(l))#     k = k + l# print("total parameters:" + str(k))

2.6 utils/visualizer.py

下面这个脚本，主要通过visdom在线画图。其中有两个方法，display_current_results方法会根据输入的xy坐标画图，display_roc会根据y_true, y_pred计算roc曲线的横纵坐标fpr, tpr，然后画图。

import visdom
import time
import numpy as np
from matplotlib import pyplot as plt
from sklearn.metrics import roc_curveclass Visualizer(object):def __init__(self, env='default', **kwargs):self.vis = visdom.Visdom(env=env, **kwargs)self.vis.close()self.iters = {}self.lines = {}def display_current_results(self, iters, x, name='train_loss'):if name not in self.iters:self.iters[name] = []if name not in self.lines:self.lines[name] = []self.iters[name].append(iters)self.lines[name].append(x)self.vis.line(X=np.array(self.iters[name]),Y=np.array(self.lines[name]),win=name,opts=dict(legend=[name], title=name))def display_roc(self, y_true, y_pred):fpr, tpr, ths = roc_curve(y_true, y_pred)self.vis.line(X=fpr,Y=tpr,# win='roc',opts=dict(legend=['roc'],title='roc'))

2.7 train.py

包含以下几个步骤：

（1）初始化数据读取dataloader（训练与测试集）

（2）初始化backbone模型

（3）初始化衡量的metric

（4）初始化优化器与学习率

（5）模型训练

（6）可视化训练过程

（7）模型测试

（8）可视化测试过程

from __future__ import print_function
import os
from data import Dataset
import torch
from torch.utils import data
import torch.nn.functional as F
from models import *
import torchvision
from utils import Visualizer, view_model
import torchVisualizer
import numpy as np
import random
import time
from config import Config
from torch.nn import DataParallel
from torch.optim.lr_scheduler import StepLR
from test import *def save_model(model, save_path, name, iter_cnt)Visualizer:save_name = os.path.join(save_path, name + '_' + str(iter_cnt) + '.pth')torch.save(model.state_dict(), save_name)return save_nameif __name__ == '__main__':opt = Config()if opt.display:visualizer = Visualizer()device = torch.device("cuda")train_dataset = Dataset(opt.train_root, opt.train_list, phase='train', input_shape=opt.input_shape)trainloader = data.DataLoader(train_dataset,batch_size=opt.train_batch_size,shuffle=True,num_workers=opt.num_workers)identity_list = get_lfw_list(opt.lfw_test_list)img_paths = [os.path.join(opt.lfw_root, each) for each in identity_list]print('{} train iters per epoch:'.format(len(trainloader)))if opt.loss == 'focal_loss':criterion = FocalLoss(gamma=2)else:criterion = torch.nn.CrossEntropyLoss()if opt.backbone == 'resnet18':model = resnet_face18(use_se=opt.use_se)elif opt.backbone == 'resnet34':model = resnet34()elif opt.backbone == 'resnet50':model = resnet50()if opt.metric == 'add_margin':metric_fc = AddMarginProduct(512, opt.num_classes, s=30, m=0.35)elif opt.metric == 'arc_margin':metric_fc = ArcMarginProduct(512, opt.num_classes, s=30, m=0.5, easy_margin=opt.easy_margin)elif opt.metric == 'sphere':metric_fc = SphereProduct(512, opt.num_classes, m=4)else:metric_fc = nn.Linear(512, opt.num_classes)# view_model(model, opt.input_shape)print(model)model.to(device)model = DataParallel(model)metric_fc.to(device)metric_fc = DataParallel(metric_fc)if opt.optimizer == 'sgd':optimizer = torch.optim.SGD([{'params': model.parameters()}, {'params': metric_fc.parameters()}],lr=opt.lr, weight_decay=opt.weight_decay)else:optimizer = torch.optim.Adam([{'params': model.parameters()}, {'params': metric_fc.parameters()}],lr=opt.lr, weight_decay=opt.weight_decay)scheduler = StepLR(optimizer, step_size=opt.lr_step, gamma=0.1)start = time.time()for i in range(opt.max_epoch):scheduler.step()model.train()for ii, data in enumerate(trainloader):data_input, label = datadata_input = data_input.to(device)label = label.to(device).long()feature = model(data_input)output = metric_fc(feature, label)loss = criterion(output, label)optimizer.zero_grad()loss.backward()optimizer.step()iters = i * len(trainloader) + iiif iters % opt.print_freq == 0:output = output.data.cpu().numpy()output = np.argmax(output, axis=1)label = label.data.cpu().numpy()# print(output)# print(label)acc = np.mean((output == label).astype(int))speed = opt.print_freq / (time.time() - start)time_str = time.asctime(time.localtime(time.time()))print('{} train epoch {} iter {} {} iters/s loss {} acc {}'.format(time_str, i, ii, speed, loss.item(), acc))if opt.display:visualizer.display_current_results(iters, loss.item(), name='train_loss')visualizer.display_current_results(iters, acc, name='train_acc')start = time.time()if i % opt.save_interval == 0 or i == opt.max_epoch:save_model(model, opt.checkpoints_path, opt.backbone, i)model.eval()acc = lfw_test(model, img_paths, identity_list, opt.lfw_test_list, opt.test_batch_size)if opt.display:visualizer.display_current_results(iters, acc, name='test_acc')

【人脸识别】arcface-pytorch代码解析相关推荐

聪明的人脸识别4——Pytorch 利用Retinaface+Facenet搭建人脸识别平台
睿智的目标检测51--Pytorch 利用Retinaface+Facenet搭建人脸识别平台学习前言什么是Retinface和Facenet 1.Retinface 2.Facenet 整体实现 ...
聪明的人脸识别3——Pytorch 搭建自己的Facenet人脸识别平台
聪明的人脸识别3--Pytorch 搭建自己的Facenet人脸识别平台学习前言什么是Facenet 源码下载 Facenet的实现思路一.预测部分 1.主干网络介绍 2.根据初步特征获得长度为 ...
基于PCA方法的ORL人脸识别及Python代码实现
基于PCA方法的ORL人脸识别及Python代码实现 PCA算法方案设计代码实现结果分析参考文献 PCA的理论知识已经有很多博客做了清晰的解释,主要概括为找到投影的面使得类间误差最大,转化为找 ...
pytorch代码解析：loss = y_hat - y.view(y_hat.size())
pytorch代码解析:pytorch中loss = y_hat - y.view(y_hat.size()) import torchy_hat = torch.tensor([[-0.0044], ...
使用PyTorch+OpenCV进行人脸识别（附代码演练）
人脸识别是一种用于从图像或视频中识别人脸的系统.它在许多应用程序和垂直行业中很有用.如今,我们看到这项技术可帮助新闻机构在重大事件报道中识别名人,为移动应用程序提供二次身份验证,为媒体和娱乐公司自动索 ...
Centerface + Facenet实现视频人脸识别（附代码）
Centerface + Facenet实现视频人脸识别 Facenet的pytorch版本:GitHub地址:https://github.com/timesler/facenet-pytorch ...
使用Delphi接入虹软人脸识别ArcFace，开发人脸库服务器
利用虹软 SDK 开发局域网人脸库服务器一.选择开发平台以前做单位食堂人脸识别就餐时,会用到在线人脸识别,终端设备必须并入互联网,单位对人脸信息比较敏感,客户会要求提供内部网人脸库使用. 利用人脸 ...
OMP算法实现人脸识别_matlab完整代码
OMP_FACE--人脸识别系统该软件包实现了基于稀疏表示的面部识别方法程序相对便捷且易上手主脚本中包含具体的一个例子通常,通常遵循以下使用顺序即可实现人脸识别功能: 选择训练数据的数据库途径 ...
ResNet论文笔记及Pytorch代码解析
注:个人学习记录感谢B站up主"同济子豪兄"的精彩讲解,参考视频的记录 [精读AI论文]ResNet深度残差网络_哔哩哔哩_bilibili 算法的意义(大概介绍) CV史上的技 ...
K210实现人脸识别（附代码解读）
基于K210的人脸识别门禁(一) 进入官网(首次登陆需要注册)获取人脸识别源码 https://wiki.sipeed.com/soft/maixpy/zh/course/ai/image/face_ ...

【人脸识别】arcface-pytorch代码解析

参考：