训练分类器?

 

1. 数据?

 

处理图像，文本，音频或视频数据时，可以使用将数据加载到 NumPy 数组中的标准 Python 包。 然后，将该数组转换为torch.*Tensor

 

• 对于图像，Pillow，OpenCV 等包很有用
• 对于音频，请使用 SciPy 和 librosa 等包
• 对于文本，基于 Python 或 Cython 的原始加载，或者 NLTK 和 SpaCy 很有用

 

专门针对视觉，一个名为torchvision的包，其中包含用于常见数据集（例如 Imagenet，CIFAR10，MNIST 等）的数据加载器，以及用于图像（即torchvision.datasets和torch.utils.data.DataLoader）的数据转换器

 

我们将使用 CIFAR10 数据集。 它具有以下类别：“飞机”，“汽车”，“鸟”，“猫”，“鹿”，“狗”，“青蛙”，“马”，“船”，“卡车”。 CIFAR-10 中的图像尺寸为3x32x32，即尺寸为32x32像素的 3 通道彩色图像

 

数据集来源：CIFAR-10 and CIFAR-100 datasets

airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck

 

由于图片地址在国外，以上图片的加载可能不如人意，大致就是这个图像：

 

2. 训练一个分类器?

 

我们将会按顺序做以下步骤：

 

1. 用torchvision 加载和标准化CIFAR10训练和测试数据
2. 定义一个神经网络
3. 定义一个损失函数
4. 使用训练数据训练网络
5. 使用测试数据测试网络

 

2.1. 加载数据并标准化?

 

使用torchvision加载CIFAR10数据十分简单：

In [1]:

import torch
import torchvision
import torchvision.transforms as transforms

 

输出的torchvision数据集是PILImage图像，其范围是[0,1]。我们将它转化为Tensor的标准范围[-1,1]

In [2]:

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=0)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=0)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

 

Files already downloaded and verified
Files already downloaded and verified

 

• 注意：如果在Windows上运行并且得到BrankPipeError，请尝试将Torch.utils.Data.Dataloader（）的Num_Worker设置为0。官网示例是Num_Worker设置为2

 

让我们显示一下训练的图片:

In [3]:

import matplotlib.pyplot as plt
import numpy as np

# functions to show an image


def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))

   

dog   frog  dog   cat  

 

2.2.定义一个卷积神经网络?

In [4]:

import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

 

2.3.定义一个损失函数和优化器?

 

让我们使用分类交叉熵损失和带有动量的 SGD

In [5]:

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

 

2.4.训练网络?

 

有趣的事情开始了，我们只需要循环我们的迭代器，并反馈到网络进行优化

In [6]:

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
            running_loss = 0.0

print('Finished Training')

 

[1,  2000] loss: 2.193
[1,  4000] loss: 1.847
[1,  6000] loss: 1.661
[1,  8000] loss: 1.569
[1, 10000] loss: 1.488
[1, 12000] loss: 1.445
[2,  2000] loss: 1.405
[2,  4000] loss: 1.355
[2,  6000] loss: 1.329
[2,  8000] loss: 1.320
[2, 10000] loss: 1.277
[2, 12000] loss: 1.250
Finished Training

 

快速保存训练模型：

In [7]:

PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)

 

2.5.使用测试集测试网络?

 

显示测试集中的图像：

In [8]:

dataiter = iter(testloader)
images, labels = dataiter.next()

# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))

   

GroundTruth:  cat   ship  ship  plane

 

加载保存的模型：

In [9]:

net = Net()
net.load_state_dict(torch.load(PATH))

Out[9]:

 

使用神经网络进行预测：

In [10]:

outputs = net(images)

In [11]:

outputs

Out[11]:

tensor([[-0.4519, -2.6896,  1.1111,  2.4411, -1.2739,  0.9407,  1.2027, -0.9218,
         -0.3061, -1.4944],
        [ 4.0095,  5.7177, -1.3274, -3.2596, -4.4239, -6.4377, -5.2835, -5.2639,
          8.8550,  3.4490],
        [ 2.2643,  1.9055,  0.2977, -1.2159, -1.5517, -2.6117, -2.5904, -2.0696,
          3.1488,  0.7971],
        [ 3.6302,  0.2553,  0.3926, -1.3850,  0.2644, -2.8077, -2.8192, -1.0332,
          1.9776,  0.4094]], grad_fn=)

 

输出是 10 类的能量。 一个类别的能量越高，网络就认为该图像属于特定类别。 因此，让我们获取最高能量的指数：

In [12]:

_, predicted = torch.max(outputs, 1)

print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'
                              for j in range(4)))

 

Predicted:  cat   ship  ship  plane

 

此次结果看起来不错

 

我们看看这个网络在整个数据集的表现：

In [13]:

correct = 0
total = 0
# since we're not training, we don't need to calculate the gradients for our outputs
with torch.no_grad():
    for data in testloader:
        images, labels = data
        # calculate outputs by running images through the network
        outputs = net(images)
        # the class with the highest energy is what we choose as prediction
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')

 

Accuracy of the network on the 10000 test images: 56 %

 

这看起来是比偶然更好（偶然的准确率是10%，即从10个类别中选择一个），看起来这个网络学到了一些东西

 

看看这个这个分类器在哪些类别分类好，哪些类别分类差：

In [14]:

# prepare to count predictions for each class
correct_pred = {classname: 0 for classname in classes}
total_pred = {classname: 0 for classname in classes}

# again no gradients needed
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predictions = torch.max(outputs, 1)
        # collect the correct predictions for each class
        for label, prediction in zip(labels, predictions):
            if label == prediction:
                correct_pred[classes[label]] += 1
            total_pred[classes[label]] += 1


# print accuracy for each class
for classname, correct_count in correct_pred.items():
    accuracy = 100 * float(correct_count) / total_pred[classname]
    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')

 

Accuracy for class: plane is 65.5 %
Accuracy for class: car   is 67.1 %
Accuracy for class: bird  is 30.4 %
Accuracy for class: cat   is 53.5 %
Accuracy for class: deer  is 44.2 %
Accuracy for class: dog   is 35.9 %
Accuracy for class: frog  is 68.2 %
Accuracy for class: horse is 70.3 %
Accuracy for class: ship  is 68.9 %
Accuracy for class: truck is 60.4 %

 

2.6.在GPU上训练?

 

如果可以使用 CUDA，首先将我们的设备定义为第一个可见的 cuda 设备：

In [15]:

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Assuming that we are on a CUDA machine, this should print a CUDA device:

print(device)

 

cuda:0

 

然后，这些方法将递归遍历所有模块，并将其参数和缓冲区转换为 CUDA 张量：

In [16]:

net.to(device)

Out[16]:

Net(
  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
  (fc1): Linear(in_features=400, out_features=120, bias=True)
  (fc2): Linear(in_features=120, out_features=84, bias=True)
  (fc3): Linear(in_features=84, out_features=10, bias=True)
)

 

还必须将每一步的输入和目标也发送到 GPU：

In [17]:

inputs, labels = data[0].to(device), data[1].to(device)

 

3.参考资料?

 

[1]Training a Classifier — PyTorch Tutorials 1.10.1+cu102 documentation

 

[2]训练分类器