• 日期: 2025-08-13
  • 标签: 常规

以下是使用MindSpore实现人脸验证网络的步骤：

  1. 数据准备

首先，需要准备人脸数据集，可以使用公共数据集，如LFW(Labeled Faces in the Wild)、CASIA-WebFace等。将数据集按照训练集和测试集的比例划分，然后将训练集和测试集分别放在不同的文件夹中。

  1. 建立网络模型

人脸验证网络通常采用Siamese网络结构，即将两个相同的卷积神经网络分别作用于两张输入图像，得到两个特征向量，然后将这两个特征向量进行比较，计算它们的距离，并预测它们是否属于同一个人。

网络模型的代码如下：

import mindspore.nn as nn
import mindspore.ops as ops

class SiameseNet(nn.Cell):
    def __init__(self):
        super(SiameseNet, self).__init__()

        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=0, has_bias=True)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=0, has_bias=True)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=0, has_bias=True)
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=0, has_bias=True)
        self.relu4 = nn.ReLU()

        self.flatten = nn.Flatten()

        self.fc1 = nn.Dense(in_channels=512 * 6 * 6, out_channels=1024, has_bias=True)
        self.relu5 = nn.ReLU()
        self.fc2 = nn.Dense(in_channels=1024, out_channels=512, has_bias=True)
        self.relu6 = nn.ReLU()

        self.fc3 = nn.Dense(in_channels=512, out_channels=128, has_bias=True)

        self.l2_distance = ops.L2Normalize(axis=1)

    def construct(self, x1, x2):
        x1 = self.pool1(self.relu1(self.conv1(x1)))
        x1 = self.pool2(self.relu2(self.conv2(x1)))
        x1 = self.pool3(self.relu3(self.conv3(x1)))
        x1 = self.relu4(self.conv4(x1))
        x1 = self.flatten(x1)
        x1 = self.relu5(self.fc1(x1))
        x1 = self.relu6(self.fc2(x1))
        x1 = self.fc3(x1)

        x2 = self.pool1(self.relu1(self.conv1(x2)))
        x2 = self.pool2(self.relu2(self.conv2(x2)))
        x2 = self.pool3(self.relu3(self.conv3(x2)))
        x2 = self.relu4(self.conv4(x2))
        x2 = self.flatten(x2)
        x2 = self.relu5(self.fc1(x2))
        x2 = self.relu6(self.fc2(x2))
        x2 = self.fc3(x2)

        x1 = self.l2_distance(x1)
        x2 = self.l2_distance(x2)

        distance = ops.PairwiseDistance(keep_dims=True)
        output = distance(x1, x2)

        return output

上述代码中，SiameseNet继承于mindspore.nn.Cell类，包含了卷积层、池化层、全连接层、L2归一化层和计算L2距离的方法，其中L2距离的计算使用mindspore.ops.PairwiseDistance实现。L2归一化层的作用是将特征向量归一化，以便于后续的距离计算。

  1. 训练模型

训练模型需要先定义损失函数和优化器，这里采用Contrastive Loss和Adam优化器。

Contrastive Loss的代码如下：

import mindspore.nn as nn
import mindspore.ops as ops

class ContrastiveLoss(nn.Cell):
    def __init__(self, margin=1.0):
        super(ContrastiveLoss, self).__init__()
        self.margin = margin
        self.relu = nn.ReLU()
        self.sum = ops.ReduceSum(keep_dims=True)
        self.sqrt = ops.Sqrt()
        self.mean = ops.ReduceMean()

    def construct(self, output, label):
        # 计算距离损失
        loss_distance = label * output ** 2 + (1 - label) * self.relu(self.margin - self.sqrt(output ** 2))
        # 计算总损失
        loss = self.mean(self.sum(loss_distance, axis=0))
        return loss

在Contrastive Loss中，需要传入距离预测值和标签，然后计算距离损失和总损失。其中，距离损失是根据标签和预测值计算得到的，如果是同一个人，则距离损失为预测值的平方，否则距离损失为预测值与margin之间的差值。总损失是所有距离损失的平均值。

Adam优化器的代码如下：

import mindspore.nn as nn
import mindspore.ops as ops

class Adam(nn.Cell):
    def __init__(self, net, learning_rate=0.001, beta1=0.9, beta2=0.999, eps=1e-8):
        super(Adam, self).__init__()
        self.net = net
        self.learning_rate = ops.Scalar(learning_rate)
        self.beta1 = ops.Scalar(beta1)
        self.beta2 = ops.Scalar(beta2)
        self.eps = ops.Scalar(eps)
        self.m = {}
        self.v = {}

        for param in self.net.trainable_params():
            self.m[param.name] = ops.Zeros()(param.shape)
            self.v[param.name] = ops.Zeros()(param.shape)

    def construct(self, x1, x2, label):
        grads = ops.GradOperation(get_by_list=True)(self.net, x1, x2, label)
        for param in self.net.trainable_params():
            m = self.beta1 * self.m[param.name] + (1 - self.beta1) * grads[param.name]
            v = self.beta2 * self.v[param.name] + (1 - self.beta2) * grads[param.name] * grads[param.name]
            m_hat = m / (1 - self.beta1 ** ops.Scalar(ops.dtype_to_pytype(self.learning_rate))(1))
            v_hat = v / (1 - self.beta2 ** ops.Scalar(ops.dtype_to_pytype(self.learning_rate))(1))
            param_update = ops.Assign()(param, param - self.learning_rate * m_hat / (self.sqrt(v_hat) + self.eps))
            self.m[param.name] = m
            self.v[param.name] = v
        return param_update

在Adam优化器中，需要传入网络、学习率和超参数beta1、beta2、eps。在每次更新参数时，需要计算梯度、更新m和v的值，然后计算m_hat和v_hat的值，最后更新参数。

训练模型的代码如下：

import mindspore as ms
import mindspore.ops as ops
from mindspore import context
from mindspore.nn import Momentum
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import LossMonitor
from mindspore.dataset.transforms import py_transforms
from mindspore.dataset.vision import Inter
from mindspore.dataset.vision import RandomCrop
from mindspore.dataset.vision import RandomHorizontalFlip
from mindspore.dataset.vision import Resize
from mindspore.dataset.vision import Normalize
from mindspore.dataset.vision import HWC2CHW
from mindspore.dataset.vision import Invert

context.set_context(mode=context.GRAPH_MODE, device_target="CPU")

# 数据集路径
train_path = './data/train'
test_path = './data/test'

# 超参数
learning_rate = 0.0005
num_epochs = 20
batch_size = 32

# 构建数据集
train_dataset = ds.ImageFolderDataset(train_path, num_parallel_workers=4, shuffle=True)
train_dataset = train_dataset.map(operations=py_transforms.Compose([
    RandomCrop([224, 224]),
    RandomHorizontalFlip(prob=0.5),
    Resize(size=[224, 224], interpolation=Inter.LINEAR),
    Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    HWC2CHW()
]), input_columns="image", num_parallel_workers=4)
train_dataset = train_dataset.batch(batch_size, drop_remainder=True, num_parallel_workers=4)

test_dataset = ds.ImageFolderDataset(test_path, num_parallel_workers=4, shuffle=False)
test_dataset = test_dataset.map(operations=py_transforms.Compose([
    Resize(size=[224, 224], interpolation=Inter.LINEAR),
    Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    HWC2CHW()
]), input_columns="image", num_parallel_workers=4)
test_dataset = test_dataset.batch(batch_size, drop_remainder=True, num_parallel_workers=4)

# 创建网络和损失函数
net = SiameseNet()
criterion = ContrastiveLoss()

# 创建优化器和学习率调度器
optimizer = Adam(net, learning_rate=learning_rate)
scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)

# 训练模型
model = Model(net, criterion, optimizer, metrics={"loss"})
model.train(num_epochs, train_dataset, callbacks=[LossMonitor(50), scheduler])

# 保存模型
config_ck = CheckpointConfig(save_checkpoint_steps=50, keep_checkpoint_max=10)
ckpoint = ModelCheckpoint(prefix="face_verification", directory="./ckpt", config=config_ck)
ckpoint.save_checkpoint(model.train_network)

上述代码中，首先定义了数据集路径和超参数，然后使用mindspore.dataset.ImageFolderDataset构建数据集，并进行数据增强和归一化处理。接着，创建了SiameseNet网络和Contrastive Loss损失函数，并使用Adam优化器进行训练。最后，使用ModelCheckpoint回调函数保存模型。

  1. 测试模型

测试模型需要先加载模型并传入测试集数据，然后计算模型在测试集上的准确率。

测试模型的代码如下：

import mindspore.dataset as ds
import mindspore.ops as ops
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net

context.set_context(mode=context.GRAPH_MODE, device_target="CPU")

# 数据集路径
test_path = './data/test'

# 超参数
batch_size = 32

# 构建数据集
test_dataset = ds.ImageFolderDataset(test_path, num_parallel_workers=4, shuffle=False)
test_dataset = test_dataset.map(operations=py_transforms.Compose([
    Resize(size=[224, 224], interpolation=Inter.LINEAR),
    Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    HWC2CHW()
]), input_columns="image", num_parallel_workers=4)
test_dataset = test_dataset.batch(batch_size, drop_remainder=True, num_parallel_workers=4)

# 加载模型
net = SiameseNet()
load_checkpoint("./ckpt/face_verification-20_156.ckpt", net=net)
net.set_train(False)

# 测试模型
correct = 0
total = 0
for data in test_dataset.create_dict_iterator():
    x1 = ops.tensor(data["image"])
    x2 = ops.tensor(data["image"])
    label = ops.tensor(data["label"])
    output = net(x1, x2)
    prediction = (output <= 0.5).astype(int)
    correct += (prediction == label).sum()
    total += label.shape[0]
accuracy = correct / total
print("Accuracy:", accuracy)

上述代码中，首先定义了测试集路径和超参数，然后使用mindspore.dataset.ImageFolderDataset构建数据集，并进行数据增强和归一化处理。接着，加载了训练好的模型，并使用测试集数据进行测试，计算测试集上的准确率。

MindSpore人脸验证网络实现：训练、测试和代码示例

原文地址: https://www.cveoy.top/t/topic/mRRI 著作权归作者所有。请勿转载和采集!

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