Pytorch从零开始实战------猴痘病识别
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。本次实验的目的是学习模型的保存和预测单张图片的结果。
第一步，导入常用包。
python
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torchvision
import torch.nn.functional as F
import torchvision.transforms as transforms
import random
import time
import numpy as np
import pandas as pd
import datetime
import gc
import pathlib
import os
import PIL
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运算的代码创建设备对象
python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device设置随机数种子
python
torch.manual_seed(428)
torch.cuda.manual_seed(428)
torch.cuda.manual_seed_all(428)
random.seed(428)
np.random.seed(428)数据集
本次实验使用猴痘病图片数据集，共2142张图片，分别为有猴痘病的图片和没有猴痘病的图片，
两种类别的图片分别存在两个文件夹中。
python
data_dir = './data/monkeydata'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classNames = [str(path).split("/")[2] for path in data_paths]
classNames # ['Monkeypox', 'Others']对数据通过dataset读取，并且将文件夹名设置为标签。
python
total_datadir = './data/monkeydata'
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]
)
])
total_data = torchvision.datasets.ImageFolder(total_datadir, transform=train_transforms)
total_data
我们可以查看所有标签
python
total_data.class_to_idx # {'Monkeypox': 0, 'Others': 1}接下来划分数据集，以8比2划分训练集和测试集
python
# 划分数据集
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])
len(train_ds), len(test_ds)随机查看5张图片
python
def plotsample(data):
fig, axs = plt.subplots(1, 5, figsize=(10, 10)) #建立子图
for i in range(5):
num = random.randint(0, len(data) - 1) #首先选取随机数，随机选取五次
#抽取数据中对应的图像对象，make_grid函数可将任意格式的图像的通道数升为3，而不改变图像原始的数据
#而展示图像用的imshow函数最常见的输入格式也是3通道
npimg = torchvision.utils.make_grid(data[num][0]).numpy()
nplabel = data[num][1] #提取标签
#将图像由(3, weight, height)转化为(weight, height, 3)，并放入imshow函数中读取
axs[i].imshow(np.transpose(npimg, (1, 2, 0)))
axs[i].set_title(nplabel) #给每个子图加上标签
axs[i].axis("off") #消除每个子图的坐标轴
plotsample(train_ds)
使用DataLoader划分批次和打乱数据集
python
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)
for X, y in test_dl:
print(X.shape) # 32, 3, 224, 224
print(y) # 1, 0, 1, 1, 1, 1, 0....
break
print(len(train_dl.dataset) + len(test_dl.dataset)) # 2142模型选择
本次实验第一次选择的是一个简单的卷积神经网络，经过卷积+卷积+池化+卷积+卷积+池化+线性层，并中间进行数据归一化处理。
python
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 12, kernel_size=5, stride=1, padding=0)
self.bn1 = nn.BatchNorm2d(12)
self.conv2 = nn.Conv2d(12, 12, kernel_size=5, stride=1, padding=0)
self.bn2 = nn.BatchNorm2d(12)
self.pool = nn.MaxPool2d(2)
self.conv3 = nn.Conv2d(12, 24, kernel_size=5, stride=1, padding=0)
self.bn3 = nn.BatchNorm2d(24)
self.conv4 = nn.Conv2d(24, 24, kernel_size=5, stride=1, padding=0)
self.bn4 = nn.BatchNorm2d(24)
self.fc1 = nn.Linear(24 * 50 * 50, len(classNames))
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = self.pool(x)
x = F.relu(self.bn3(self.conv3(x)))
x = F.relu(self.bn4(self.conv4(x)))
x = self.pool(x)
x = x.view(-1, 24 * 50 * 50)
x = self.fc1(x)
return x;使用summary查看模型
python
from torchsummary import summary
# 将模型转移到GPU中
model = Model().to(device)
summary(model, input_size=(3, 224, 224))模型训练
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定义一些超参数，经实验，将学习率设置为0.01效果最好。
python
loss_fn = nn.CrossEntropyLoss()
learn_rate = 0.01
opt = torch.optim.SGD(model.parameters(), lr=learn_rate)开始训练，epochs设置为20，并且将训练集的最优结果保存。
python
import time
epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []
T1 = time.time()
best_acc = 0
PATH = './my_model.pth'
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
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
torch.save(model.state_dict(), PATH)
print("model save")
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))
print("Done")
T2 = time.time()
print('程序运行时间:%s毫秒' % ((T2 - T1)*1000))可以看到，最好的时候，测试集准确率达到百分之91.8
数据可视化
使用matplotlib进行数据可视化。
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()
其他模型
本次实验也使用了ResNet模型，虽然参数量较大，但训练效果较好
定义模型
python
class Model(nn.Module):
def __init__(self):
super().__init__()
# 创建预训练的ResNet-18模型
self.resnet = torchvision.models.resnet18(pretrained=True)
# 将ResNet的最后一层（全连接层）替换为适合二分类问题的新全连接层
self.resnet.fc = nn.Linear(self.resnet.fc.in_features, len(classes))
def forward(self, x):
return self.resnet(x)
from torchsummary import summary
# 将模型转移到GPU中
model = Model().to(device)经实验，把学习率设置为0.001，效果较好
python
import time
epochs = 50
train_loss = []
train_acc = []
test_loss = []
test_acc = []
loss_fn = nn.CrossEntropyLoss()
learn_rate = 0.001
opt = torch.optim.SGD(model.parameters(), lr=learn_rate)
T1 = time.time()
best_acc = 0
PATH = './my_model.pth'
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
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
torch.save(model.state_dict(), PATH)
print("model save")
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))
print("Done")
T2 = time.time()
print('程序运行时间:%s毫秒' % ((T2 - T1)*1000))最终在测试集的准确率可达到97.2%。
可视化训练过程
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()
图片预测
img_path：要进行预测的图像文件的路径。
model：用于进行图像分类预测的深度学习模型。
transform：用于对图像进行预处理的数据转换函数。
classes：包含类别标签的列表，用于将模型的输出索引映射回类别标签。
大致意思是图像与训练时的输入数据格式相匹配，模型接受批量输入，因此我们需要在维度上添加一个批次维度，从而进行预测
python
classes = list(total_data.class_to_idx)
def predict_img(img_path, model, transform, classes):
test_img = Image.open(img_path).convert('RGB')
test_img = transform(test_img)
img = test_img.to(device).unsqueeze(0)
model.eval()
output = model(img)
_, pred = torch.max(output, 1) # 在张量的第一个维度上取最大值操作
pred_class = classes[pred]
print(f'预测结果是：{pred_class}')开始预测
python
predict_img(img_path='./data/monkeydata/Monkeypox/M01_01_00.jpg',
model=model,
transform=train_transforms,
classes=classes)
# 预测结果是：Monkeypox