- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者:K同学啊
我的环境
语言环境:python 3.7.12
编译器:pycharm
深度学习环境:tensorflow 2.7.0
数据:本地数据集

一、代码
# 第P6周:VGG-16算法-Pytorch实现人脸识别
# 数据集:dataset_facerecognition
# 任务:1. 保存训练过程中的最佳模型权重
# 2. 调用官方的VGG-16网络框架
# 设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warnings
warnings.filterwarnings("ignore") #忽略警告信息
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
# 导数据
import os,PIL,random,pathlib
data_dir = './dataset_facerecognition/'
data_dir = pathlib.Path(data_dir) # 其下封装了很多功能,使用方便安全
data_paths = list(data_dir.glob('*'))
classNames = [str(path).split("/")[1] for path in data_paths] #获取子目录名称
print(classNames)
# 关于transforms.Compose的更多介绍可以参考:https://blog.youkuaiyun.com/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸
# transforms.RandomHorizontalFlip(), # 随机水平翻转
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor,并归一化到[0,1]之间
transforms.Normalize( # 标准化处理-->转换为标准正太分布(高斯分布),使模型更容易收敛
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
total_data = datasets.ImageFolder(data_dir,transform=train_transforms)
print(total_data)
print(total_data.class_to_idx)
# 划分数据集
train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_dataset, test_dataset)
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
for X, y in test_dl:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
# 调用官方得VGG16
from torchvision.models import vgg16
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
# 加载预训