天气识别-第三周

• 本文为365天深度学习训练营 中的学习记录博客
• 参考文章：Pytorch实战 | 第P3周：彩色图片识别：天气识别
• 原作者：K同学啊|接辅导、项目定制

难度：新手入门⭐
语言：Python3、Pytorch

要求：√

本地读取并加载数据。
测试集accuracy到达93%

拔高：√

测试集accuracy到达95%
调用模型识别一张本地图片

我的环境：
语言环境：Python 3.6.13
编译器：Pycharm 2020.2
深度学习环境：Pytorch 1.10.0
显卡及显存: RTX 3060(服务器)

---

一、 前期准备

1. 设置GPU

如果设备上支持GPU就使用GPU,否则使用CPU

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

2. 导入数据

import os,PIL,random,pathlib

data_dir = './data/weather_photos/'
data_dir = pathlib.Path(data_dir)

data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[1] for path in data_paths]
classeNames
['cloudy', 'rain', 'shine', 'sunrise']
total_datadir = './data/'

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

3. 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])


```python
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

二、构建CNN网络

import torch.nn.functional as F

class Network_bn(nn.Module):
    def __init__(self):
        super(Network_bn, self).__init__()
        """
        nn.Conv2d()函数：
        第一个参数（in_channels）是输入的channel数量
        第二个参数（out_channels）是输出的channel数量
        第三个参数（kernel_size）是卷积核大小
        第四个参数（stride）是步长，默认为1
        第五个参数（padding）是填充大小，默认为0
        """
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=0)
        self.bn1 = nn.BatchNorm2d(12)
        self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=0)
        self.bn2 = nn.BatchNorm2d(12)
        self.pool = nn.MaxPool2d(2,2)

        self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=0)
        self.bn4 = nn.BatchNorm2d(24)
        self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=0)
        self.bn5 = nn.BatchNorm2d(24)

        self.conv6 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=5, stride=1, padding=0)
        self.bn6 = nn.BatchNorm2d(48)
        self.conv7 = nn.Conv2d(in_channels=48, out_channels=48, kernel_size=5, stride=1, padding=0)
        self.bn7 = nn.BatchNorm2d(48)
        self.fc1 = nn.Linear(48*21*21, len(classeNames))

    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.bn4(self.conv4(x)))
        x = F.relu(self.bn5(self.conv5(x)))
        x = self.pool(x)   #24*50*50
        x = F.relu(self.bn6(self.conv6(x)))    #48*46*46
        x = F.relu(self.bn7(self.conv7(x)))    #48*42*42
        x = self.pool(x)  #48*21*21
        x = x.view(-1, 48*21*21)
        x = self.fc1(x)

        return x

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

`

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

model = Network_bn().to(device)
model

三、 训练模型

1. 设置超参数

loss_fn    = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 1e-4 # 学习率
opt        = torch.optim.SGD(model.parameters(),lr=learn_rate)

2.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

3. 编写测试函数

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

4. 正式训练

epochs     = 20
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')

我们可以看到，20epoch时测试集准确率已经达到93.3%，已基本满足需求.为了继续提升模型准确率，
原有模型加入（卷积层-BN层-卷积层-BN层)

class Network_bn(nn.Module):
    def __init__(self):
        super(Network_bn, self).__init__()
......
    self.conv6 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=5, stride=1, padding=0)
    self.bn6 = nn.BatchNorm2d(48)
    self.conv7 = nn.Conv2d(in_channels=48, out_channels=48, kernel_size=5, stride=1, padding=0)
    self.bn7 = nn.BatchNorm2d(48)
   self.fc1 = nn.Linear(48*21*21, len(classeNames))

   def forward(self, x):
   ........
        x = F.relu(self.bn6(self.conv6(x)))    #48*46*46
        x = F.relu(self.bn7(self.conv7(x)))    #48*42*42
        x = self.pool(x)  #48*21*21
        x = x.view(-1, 48*21*21)

并将优化器改为Adam

opt        = torch.optim.Adam(model.parameters(),lr=learn_rate)

并调整训练次数为100，训练结果如下图:

可视化结果：

模型在40epoch时测试集准确率已达到95.0%，并在此处上下波动。
调用本地图片测试：
首先保存模型

torch.save(model.state_dict(),"./net_19.pth")
model = torch.load("./net_19.pth")
print(model)

##载入模型并读取权重

##载入模型并读取权重
model = Network_bn()
model.load_state_dict(torch.load("./net_19.pth"))
model.to(device)
model.eval()

img_path = './data/cloudy_08099.jpg'  #本地图片路径

输出概率最大的类别

_, indices = torch.max(outputs, 1)
percentage = torch.nn.functional.softmax(outputs, dim=1)[0] * 100
perc = percentage[int(indices)].item()
result = class_names[indices]
print('predicted:', result)

识别结果

总结

1.学习torchvision.transforms.Compose()类。这个类的主要作用是串联多个图片变换的操作。也学习了Batch Normalization，它通过引入层内的批归一化操作对特征进行归一化，减少ICS（Internal Covariate Shift），实现了加速网络收敛的效果。
2.20epoch时模型准确率已经达到93.3%，满足基本要求。模型改进后在40epoch时模型准确率达到95.0%。
3.保存模型，载入模型并读取权重后，即可识别本地图片。

参考资料
pytorch模型推理单张图片读取方式