第P3周：Pytorch实现天气识别
- 🍨 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
目标
- 读取天气图片，按文件夹分类
- 搭建CNN网络，保存网络模型并加载模型
- 使用保存的模型预测真实天气
具体实现
（一）环境
语言环境：Python 3.10
编 译 器: PyCharm
框 架: Pytorch 2.5.1
（二）具体步骤
1. 通用文件Utils.py
import torch
# 第一步：设置GPU
def USE_GPU():
if torch.cuda.is_available():
print('CUDA is available, will use GPU')
device = torch.device("cuda")
else:
print('CUDA is not available. Will use CPU')
device = torch.device("cpu")
return device
2. 模型代码
import os
from torchinfo import summary
from Utils import USE_GPU
import pathlib
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import datasets
device = USE_GPU()
# 导入数据
data_dir = './data/weather_photos/'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
# print(data_paths)
classNames = [str(path).split("\\")[2] for path in data_paths]
print(classNames)
# 查看一下图片
image_folder = './data/weather_photos/cloudy'
# 获取image_folder下的所有图片
image_files = [f for f in os.listdir(image_folder) if f.endswith((".jpg", ".png", ".jpeg"))]
#创建matplotlib图像
fig, axes = plt.subplots(3, 8, figsize=(16, 6))
for ax, img_file in zip(axes.flat, image_files):
img_path = os.path.join(image_folder, img_file)
img = Image.open(img_path)
ax.imshow(img)
ax.axis('off')
plt.tight_layout()
plt.title(image_folder, loc='center')
# plt.show()
train_transforms = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片统一resize成224大小
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
total_data = datasets.ImageFolder(data_dir, transform=train_transforms)
print(total_data)
# 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_size, test_size)
print(train_dataset, test_dataset)
# 设置dataloader
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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
# 构建CNN网络
import torch.nn.functional as F
class Network_bn(nn.Module):
def __init__(self):
super(Network_bn, self).__init__()
self.conv1 = nn.Conv2d(3, 12, 5, 1, 0)
self.bn1 = nn.BatchNorm2d(12)
self.conv2 = nn.Conv2d(12, 12, 5, 1, 0)
self.bn2 = nn.BatchNorm2d(12)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv4 = nn.Conv2d(12, 24, 5, 1, 0)
self.bn4 = nn.BatchNorm2d(24)
self.conv5 = nn.Conv2d(24, 24, 5, 1, 0)
self.bn5 = nn.BatchNorm2d(24)
self.pool2 = nn.MaxPool2d(2, 2)
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.pool1(x)
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = self.pool2(x)
x = x.view(-1, 24 * 50 * 50)
x = self.fc1(x)
return x
model = Network_bn().to(device)
print(model)
summary(model)
# 训练模型
loss_fn = nn.CrossEntropyLoss()
learn_rate = 1e-4
opt = torch.optim.SGD(model.parameters(), lr=learn_rate)
# 循环训练
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
num_batches = len(dataloader)
train_loss, train_acc = 0, 0
for X, y in dataloader:
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.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
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_loss, test_acc = 0, 0
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
epochs = 25
train_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
template = 'Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}'
print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss))
print('Done')
# 结果可视化
import matplotlib.pyplot as plt
#隐藏警告
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()
# 保存模型
torch.save(model, "./models/cnn-weather.pth")
3. 预测真实图片：pred.py
from pydoc import classname
from PIL import Image
from matplotlib import pyplot as plt
from torch import nn
from Utils import USE_GPU
import torch
import torchvision.transforms as transforms
from torchvision import datasets
import pathlib
device = USE_GPU()
# 构建CNN网络
import torch.nn.functional as F
class Network_bn(nn.Module):
def __init__(self):
super(Network_bn, self).__init__()
self.conv1 = nn.Conv2d(3, 12, 5, 1, 0)
self.bn1 = nn.BatchNorm2d(12)
self.conv2 = nn.Conv2d(12, 12, 5, 1, 0)
self.bn2 = nn.BatchNorm2d(12)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv4 = nn.Conv2d(12, 24, 5, 1, 0)
self.bn4 = nn.BatchNorm2d(24)
self.conv5 = nn.Conv2d(24, 24, 5, 1, 0)
self.bn5 = nn.BatchNorm2d(24)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(24 * 50 * 50, 4)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = self.pool1(x)
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = self.pool2(x)
x = x.view(-1, 24 * 50 * 50)
x = self.fc1(x)
return x
model = torch.load('./models/cnn-weather.pth', weights_only=False)
model.eval()
transform = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片统一resize成224大小
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
className = ['cloudy', 'rain', 'shine', 'sunshine']
# 导入数据
weather_data_directory = './mydata/weather'
weather_data_directory = pathlib.Path(weather_data_directory)
print(weather_data_directory)
image_count = len(list(weather_data_directory.glob('*.jpg')))
print("待识别天气图片数量：", image_count)
plt.figure(figsize=(5, 3))
i = 0
for path in weather_data_directory.glob('*.jpg'):
print(path) # 天气图片路径
image_source = Image.open(path) # 打开图片转换成图片数据
image = transform(image_source)
image = image.unsqueeze(0) # 增加维度
print(image.shape)
output = model(image.to(device))
pred = className[torch.argmax(output, dim=1).item()]
print(pred)
plt.subplot(2, 5, i+1)
plt.imshow(image_source)
plt.title(pred)
plt.xticks([])
plt.yticks([])
i += 1
plt.show()
准确率80%.
（三）总结
下载一个大数据集训练一下，数据如下：
- 晴天：10000张
- 多云：10000张
- 雨天：10000张
- 大雪：10000张
- 薄雾：10000张
- 雷雨：10000张
经历漫长的几个小时训练，结果：
