Pytorch从零开始实战------明星识别
本系列来源于365天深度学习训练营
原作者K同学
文章目录
环境准备
本文基于Jupyter notebook，使用Python3.8，Pytorch2.0.1+cu118，torchvision0.15.2，需读者自行配置好环境且有一些深度学习理论基础。本次实验的目的是了解如何设置动态学习率。
第一步，导入常用包。
python
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.nn.functional as F
import random
from time import time
import numpy as np
import pandas as pd
import datetime
import gc
import os
import copy
os.environ['KMP_DUPLICATE_LIB_OK']='True' # 用于避免jupyter环境突然关闭
torch.backends.cudnn.benchmark=True # 用于加速GPU运算的代码设置随机数种子
python
torch.manual_seed(55)
torch.cuda.manual_seed(55)
torch.cuda.manual_seed_all(55)
random.seed(55)
np.random.seed(55)创建设备对象
python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device # device(type='cuda')数据集
本次数据集使用的一系列明星图片，每一位明星的图片存放在对应的文件夹中，文件夹名为明星的姓名。
使用pathlib查看类别名称
python
import pathlib
data_dir = './data/star'
data_dir = pathlib.Path(data_dir) # 转成pathlib.Path对象
data_paths = list(data_dir.glob('*'))
classNames = [str(path).split("/")[2] for path in data_paths]
classNames
使用transforms将图片进行预处理，并且使用datasets整合数据集，每个姓名标签对应的一个数字标签。
python
train_transforms = transforms.Compose([
transforms.Resize([224, 224]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 标准化
])
total_data = datasets.ImageFolder("./data/star/", transform=train_transforms)
total_data.class_to_idx
查看随机五张图片。
将数据集以8比2划分为训练集和测试集，使用DataLoader划分批次和随机打乱。
python
train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size
train_ds, test_ds = torch.utils.data.random_split(total_data, [train_size, test_size])
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_ds,
batch_size=batch_size,
shuffle=True,
)
test_dl = torch.utils.data.DataLoader(test_ds,
batch_size=batch_size,
shuffle=True,
)
len(train_dl.dataset), len(test_dl.dataset) # (1440, 360)模型选择
本次实验我们直接调用官方的数据集，使用官方预训练的VGG16，冻结模型参数，只训练最后一层的参数。
python
# 调用官方vgg16
from torchvision.models import vgg16
model = vgg16(pretrained = True).to(device) # 加载预训练的vgg16模型
for param in model.parameters():
param.requires_grad = False # 冻结模型的参数，只训练最后一层的参数
model.classifier._modules['6'] = nn.Linear(4096, len(classNames)) # 修改vgg16模型中最后一层全连接层，输出目标类别个数
model.to(device)
model
创建模型，使用summary查看参数，VGG16的参数还是比较多的。
python
from torchsummary import summary
# 将模型转移到GPU中
model = model.to(device)
summary(model, input_size=(3, 224, 224))
开始训练
定义训练函数
python
def train(dataloader, model, loss_fn, opt):
size = len(dataloader.dataset)
num_batches = len(dataloader)
train_acc, train_loss = 0, 0
for X, y in dataloader:
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred, y)
opt.zero_grad()
loss.backward()
opt.step()
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss定义测试函数
python
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_acc, test_loss = 0, 0
with torch.no_grad():
for X, y in dataloader:
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred, y)
test_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss += loss.item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss设置超参数，本次使用官方的学习率衰减，学习率每经过 4 个 epoch 就会以 0.92 的指数衰减。
python
lambda1 = lambda epoch: 0.92 ** (epoch // 4)
loss_fn = nn.CrossEntropyLoss()
learn_rate = 0.001
opt = torch.optim.SGD(model.parameters(), lr=learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(opt, lr_lambda=lambda1) # 选定调整方法开始训练，可能是因为只训练最后一层，模型学习的不是很好。
python
import time
epochs = 30
train_loss = []
train_acc = []
test_loss = []
test_acc = []
T1 = time.time()
best_acc = 0
best_model = 0
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
scheduler.step()
model.eval() # 确保模型不会进行训练操作
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
if epoch_test_acc > best_acc:
best_acc = epoch_test_acc
best_model = copy.deepcopy(model)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
print("epoch:%d, train_acc:%.1f%%, train_loss:%.3f, test_acc:%.1f%%, test_loss:%.3f"
% (epoch + 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss))
T2 = time.time()
print('程序运行时间:%s毫秒' % ((T2 - T1)*1000))
PATH = './best_model.pth' # 保存的参数文件名
if best_model is not None:
torch.save(best_model.state_dict(), PATH)
print('保存最佳模型')
print("Done")
模型可视化
可视化函数
python
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 #分辨率
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
模型预测
定义预测函数
python
from PIL import Image
classes = list(total_data.class_to_idx)
def predict_one_image(image_path, model, transform, classes):
test_img = Image.open(image_path).convert('RGB')
plt.imshow(test_img) # 展示预测的图片
test_img = transform(test_img)
img = test_img.to(device).unsqueeze(0)
model.eval()
output = model(img)
_,pred = torch.max(output,1)
pred_class = classes[pred]
print(f'预测结果是：{pred_class}')调用函数，使用模型预测图片
python
predict_one_image(image_path='./data/star/Angelina Jolie/001_fe3347c0.jpg',
model=model,
transform=train_transforms,
classes=classes)
# 预测结果是：Angelina Jolie
使用保存的最佳模型查看一下损失。
python
best_model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
epoch_test_acc, epoch_test_loss # (0.3861111111111111, 1.9115476707617443)总结
本次调用官方预训练的VGG模型，由于VGG的参数量过大，我们仅训练了最后一层，所以效果不是很好，所以未来数据集比较大的时候，可以放开所有的层重新训练。