基于Python的笑脸识别
from train_model.model import FaceCNNfrom data_set.FaceData import FaceDataset
import torch
import torch.utils.data as data
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
import cv2
# 验证模型在验证集上的正确率
def validate(model, dataset, batch_size):
val_loader = data.DataLoader(dataset, batch_size)
result, num = 0.0, 0
for images, labels in val_loader:
pred = model.forward(images)
pred = np.argmax(pred.data.numpy(), axis=1)
labels = labels.data.numpy()
result += np.sum((pred == labels))
num += len(images)
acc = result / num
return acc
def train(train_dataset, val_dataset, batch_size, epochs, learning_rate, wt_decay):
# 载入数据并分割batch
train_loader = data.DataLoader(train_dataset, batch_size)
# 构建模型
model = FaceCNN()
# 损失函数
loss_function = nn.CrossEntropyLoss()
# 优化器
optimizer = optim.SGD(model.parameters(), lr=learning_rate, weight_decay=wt_decay)
# 学习率衰减
# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.8)
# 逐轮训练
for epoch in range(epochs):
# 记录损失值
loss_rate = 0
# scheduler.step() # 学习率衰减
model.train()# 模型训练
for images, labels in train_loader:
# 梯度清零
optimizer.zero_grad()
# 前向传播
output = model.forward(images)
# 误差计算
loss_rate = loss_function(output, labels)
# 误差的反向传播
loss_rate.backward()
# 更新参数
optimizer.step()
# 打印每轮的损失
print('After {} epochs , the loss_rate is : '.format(epoch + 1), loss_rate.item())
model.eval()# 模型评估
acc_train = validate(model, train_dataset, batch_size)
acc_val = validate(model, val_dataset, batch_size)
print('After {} epochs , the acc_train is : '.format(epoch + 1), acc_train)
print('After {} epochs , the acc_val is : '.format(epoch + 1), acc_val)
if epoch % 5 == 0:
# model.eval()# 模型评估
# acc_train = validate(model, train_dataset, batch_size)
# acc_val = validate(model, val_dataset, batch_size)
# print('After {} epochs , the acc_train is : '.format(epoch + 1), acc_train)
# print('After {} epochs , the acc_val is : '.format(epoch + 1), acc_val)
torch.save(model.state_dict(), 'C:\\Users\\bhj\\Desktop\\smile_train\\moudles\\model_net%d.pth'%epoch)
return model
def main():
# 数据集实例化(创建数据集)
data_train_path = "C:\\Users\\bhj\\Desktop\\smile_train\\datasets\\train_data"
data_val_path = "C:\\Users\\bhj\\Desktop\\smile_train\\datasets\\val_data"
train_csv = "C:\\Users\\bhj\\Desktop\\smile_train\\train_data.csv"
val_csv = "C:\\Users\\bhj\\Desktop\\smile_train\\val_data.csv"
train_dataset = FaceDataset(data_train_path, train_csv)
val_dataset = FaceDataset(data_val_path, val_csv)
# 超参数可自行指定
model = train(train_dataset, val_dataset, batch_size=32, epochs=100, learning_rate=0.01, wt_decay=0)
# 保存模型
torch.save(model.state_dict(), 'model_net_result.pth')
if __name__ == '__main__':
main()
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