在PyTorch中搭建卷積神經網絡通常涉及以下步驟：

1. 導入必要的庫和模塊：

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

2. 定義卷積神經網絡模型類：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(32*7*7, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        x = x.view(-1, 32*7*7)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

3. 實例化模型類并定義損失函數和優化器：

model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

4. 訓練模型：

for epoch in range(num_epochs):
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

5. 測試模型：

correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = correct / total
print('Accuracy: {:.2f}%'.format(100 * accuracy))

以上是一個簡單的卷積神經網絡的搭建過程，你可以根據具體的任務和數據集自行調整網絡結構和超參數。