Python+pytorch实现天气识别
作者：重邮研究森
这篇文章主要为大家详细介绍了如何利用Python+pytorch实现天气识别功能，文中的示例代码讲解详细，具有一定的借鉴价值，需要的可以参考一下
一、前期工作
环境：python3.6，1080ti，pytorch1.10（实验室服务器的环境）
1.设置GPU或者cpu
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torchvision
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
2.导入数据
import os,PIL,random,pathlib
data_dir = 'weather_photos/'
data_dir = pathlib.Path(data_dir)
print(data_dir)
data_paths = list(data_dir.glob('*'))
print(data_paths)
classeNames = [str(path).split("/")[1] for path in data_paths]
classeNames
二、数据预处理
数据格式设置
total_datadir = 'weather_photos/'
# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/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.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(total_datadir,transform=train_transforms)
total_data
数据集划分
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]) train_dataset, test_dataset
设置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)
检查数据格式
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
三、搭建网络
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential,ReLU
num_classes = 4
class Model(nn.Module):
def __init__(self):
super(Model,self).__init__()
# 卷积层
self.layers = Sequential(
# 第一层
nn.Conv2d(3, 24, kernel_size=5),
nn.BatchNorm2d(24),
nn.ReLU(),
# 第二层
nn.Conv2d(24,64 , kernel_size=5),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Conv2d(64, 128, kernel_size=5),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 24, kernel_size=5),
nn.BatchNorm2d(24),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Flatten(),
nn.Linear(24*50*50, 516,bias=True),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(516, 215,bias=True),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(215, num_classes,bias=True),
)
def forward(self, x):
x = self.layers(x)
return x
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
model = Model().to(device)
model打印网络结构
四、训练模型
1.设置学习率
loss_fn = nn.CrossEntropyLoss() # 创建损失函数 learn_rate = 1e-3 # 学习率 opt = torch.optim.SGD(model.parameters(),lr=learn_rate)
2.模型训练
训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小，一共60000张图片
num_batches = len(dataloader) # 批次数目，1875（60000/32）
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 获取图片及其标签
X, y = X.to(device), y.to(device)
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # grad属性归零
loss.backward() # 反向传播
optimizer.step() # 每一步自动更新
# 记录acc与loss
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
def test (dataloader, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小，一共10000张图片
num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，向上取整）
test_loss, test_acc = 0, 0
# 当不进行训练时，停止梯度更新，节省计算内存消耗
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 计算loss
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具体训练代码
epochs = 30
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')
五、模型评估
1.Loss和Accuracy图
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()
2.对结果进行预测
import os
import json
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
img_path = "weather_photos/cloudy/cloudy1.jpg"
classes = ['cloudy', 'rain', 'shine', 'sunrise']
data_transform = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸
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。
])
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
img = Image.open(img_path)
plt.imshow(img)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
model.eval()
with torch.no_grad():
# predict class
output = torch.squeeze(model(img.to(device))).cpu()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
print(classes[predict_cla])
plt.show()
if __name__ == '__main__':
main()预测结果如下：
3.总结
1.本次能主要对以下函数进行了学习
| transforms.Compose | 针对数据转换，例如尺寸，类型 |
| datasets.ImageFolder | 结合上面这个对某文件夹下数据处理 |
| torch.utils.data.DataLoader | 设置dataset |
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