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beginner/blitz/cifar10_tutorial

在 Google Colab 中執行

注意

請跳轉到末尾下載完整的示例程式碼。

訓練分類器#

建立日期：2017 年 3 月 24 日 | 最後更新：2025 年 9 月 30 日 | 最後驗證：未經驗證

就是這樣。您已經瞭解瞭如何定義神經網路、計算損失以及更新網路權重。

現在您可能會想，

資料呢？#

通常，當您需要處理影像、文字、音訊或影片資料時，可以使用標準的 Python 包將資料載入到 NumPy 陣列中。然後，您可以將此陣列轉換為 torch.*Tensor。

對於影像，Pillow、OpenCV 等包非常有用。
對於音訊，scipy 和 librosa 等包非常有用。
對於文字，可以使用純 Python 或基於 Cython 的載入，或者 NLTK 和 SpaCy。

特別是對於視覺領域，我們建立了一個名為 torchvision 的包，它提供了常見資料集（如 ImageNet、CIFAR10、MNIST 等）的資料載入器以及影像資料轉換器，即 torchvision.datasets 和 torch.utils.data.DataLoader。

這提供了極大的便利，並避免了編寫樣板程式碼。

在本教程中，我們將使用 CIFAR10 資料集。它包含以下類別：“飛機”、“汽車”、“鳥”、“貓”、“鹿”、“狗”、“青蛙”、“馬”、“船”、“卡車”。CIFAR-10 中的影像尺寸為 3x32x32，即 3 通道的彩色影像，尺寸為 32x32 畫素。

訓練影像分類器#

我們將按順序執行以下步驟：

使用 torchvision 載入和歸一化 CIFAR10 訓練集和測試集。
定義一個卷積神經網路。
定義一個損失函式。
在訓練資料上訓練網路。
在測試資料上測試網路。

1. 載入和歸一化 CIFAR10#

使用 torchvision 載入 CIFAR10 非常簡單。

import torch
import torchvision
import torchvision.transforms as transforms

torchvision 資料集的輸出是 PILImage 影像，範圍為 [0, 1]。我們將其轉換為歸一化範圍 [-1, 1] 的 Tensor。

注意

如果您在 Windows 或 MacOS 上執行此教程，並遇到與多程序相關的 BrokenPipeError 或 RuntimeError，請嘗試將 `torch.utils.data.DataLoader()` 的 `num_worker` 設定為 0。

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=2)

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=2)

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

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為了好玩，我們來展示一些訓練影像。

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 = next(dataiter)

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

truck plane plane horse

2. 定義卷積神經網路#

從“神經網路”部分複製神經網路，並修改它以接受 3 通道影像（而不是之前定義的 1 通道影像）。

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()

3. 定義損失函式和最佳化器#

讓我們使用分類交叉熵損失和帶動量的 SGD。

import torch.optim as optim

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

4. 訓練網路#

這時事情開始變得有趣起來。我們只需遍歷資料迭代器，將輸入饋送到網路並進行最佳化。

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: 2.171
[1,  4000] loss: 1.850
[1,  6000] loss: 1.662
[1,  8000] loss: 1.594
[1, 10000] loss: 1.540
[1, 12000] loss: 1.500
[2,  2000] loss: 1.422
[2,  4000] loss: 1.386
[2,  6000] loss: 1.369
[2,  8000] loss: 1.329
[2, 10000] loss: 1.310
[2, 12000] loss: 1.307
Finished Training

讓我們快速儲存訓練好的模型。

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

有關儲存 PyTorch 模型的更多詳細資訊，請參見此處。

5. 在測試資料上測試網路#

我們對訓練資料集進行了 2 個週期的訓練。但我們需要檢查網路是否真的學到了一些東西。

我們將透過預測神經網路輸出的類別標籤，並將其與真實標籤進行比較來檢查這一點。如果預測正確，我們就將該樣本新增到正確預測列表。

好的，第一步。讓我們顯示一張測試集影像，以便熟悉一下。

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

# 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

接下來，讓我們重新載入儲存的模型（注意：這裡不一定需要儲存和重新載入模型，我們只是為了演示如何操作）。

net = Net()
net.load_state_dict(torch.load(PATH, weights_only=True))

<All keys matched successfully>

好的，現在讓我們看看神經網路如何看待上述示例。

outputs = net(images)

輸出是 10 個類別的能量值。一個類別的能量值越高，網路就越認為該影像屬於該特定類別。因此，讓我們獲取最高能量的索引。

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

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

Predicted:  cat   car   car   plane

結果看起來相當不錯。

讓我們看看網路在整個資料集上的表現。

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, 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 %

這看起來比隨機猜測（隨機從 10 個類別中選擇一個，準確率為 10%）好多了。看來網路學到了一些東西。

嗯，哪些類別表現好，哪些類別表現不好？

# 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 64.7 %
Accuracy for class: car   is 71.7 %
Accuracy for class: bird  is 42.9 %
Accuracy for class: cat   is 40.5 %
Accuracy for class: deer  is 52.4 %
Accuracy for class: dog   is 53.2 %
Accuracy for class: frog  is 61.4 %
Accuracy for class: horse is 65.0 %
Accuracy for class: ship  is 57.4 %
Accuracy for class: truck is 55.5 %

好的，那麼接下來呢？

如何在 GPU 上執行這些神經網路？

在 GPU 上訓練#

就像將 Tensor 傳輸到 GPU 一樣，您也可以將神經網路傳輸到 GPU。

如果我們有 CUDA 可用，讓我們首先定義我們的裝置為第一個可見的 CUDA 裝置。

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

本節的其餘部分假設 `device` 是一個 CUDA 裝置。

然後，這些方法將遞迴地遍歷所有模組，並將它們的引數和緩衝區轉換為 CUDA Tensor。

net.to(device)

請記住，您需要在每一步將輸入和目標也傳送到 GPU。

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

為什麼我沒有注意到與 CPU 相比有巨大的速度提升？因為您的網路非常小。

練習： 嘗試增加網路的寬度（第一個 `nn.Conv2d` 的第二個引數，以及第二個 `nn.Conv2d` 的第一個引數——它們需要相同），看看能獲得多大的速度提升。

已達成目標:

高層次地理解 PyTorch 的 Tensor 庫和神經網路。
訓練一個小神經網路來對影像進行分類。

在多個 GPU 上訓練#

如果您想使用所有 GPU 獲得更大的速度提升，請參閱可選：資料並行性。

接下來去哪裡？#

del dataiter

指令碼總執行時間： (1 分鐘 57.226 秒)