CentOS上PyTorch可视化工具推荐

在CentOS上，有几个推荐的PyTorch可视化工具，可以帮助您更好地理解和调试深度学习模型。以下是一些常用的工具及其使用方法：

hiddenlayer

• 功能：用于可视化神经网络的结构。
• 安装：pip install hiddenlayer
• 使用示例：

  import hiddenlayer as h
  import torch
  
  class convnet(nn.Module):
      def __init__(self):
          super(ConvNet, self).__init__()
          self.conv1 = nn.Sequential(
              nn.Conv2d(1, 16, 3, 1, 1),
              nn.ReLU(),
              nn.AvgPool2d(2, 2)
          )
          self.conv2 = nn.Sequential(
              nn.Conv2d(16, 32, 3, 1, 1),
              nn.ReLU(),
              nn.MaxPool2d(2, 2)
          )
          self.fc = nn.Sequential(
              nn.Linear(32 * 7 * 7, 128),
              nn.ReLU(),
              nn.Linear(128, 64),
              nn.ReLU()
          )
          self.out = nn.Linear(64, 10)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.conv2(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
          return self.out(x)
  
  myconvnet = convnet()
  hvis_graph = h.build_graph(myconvnet, torch.zeros([1, 1, 28, 28]))
  vis_graph.theme = h.graph.themes["blue"].copy()
  vis_graph.save("./demo1.png")
  

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torchviz

• 功能：用于可视化神经网络的结构和计算图。
• 安装：pip install torchviz
• 使用示例：

  from torch import nn
  from torchviz import make_dot
  
  class simplecnn(nn.Module):
      def __init__(self):
          super(simplecnn, self).__init__()
          self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
          self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
          self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
          self.fc1 = nn.Linear(64 * 28 * 28, 128)
          self.fc2 = nn.Linear(128, 10)
  
      def forward(self, x):
          x = self.conv1(x)
          x = torch.relu(x)
          x = torch.max_pool2d(x, 2, 2)
          x = self.conv2(x)
          x = torch.relu(x)
          x = torch.max_pool2d(x, 2, 2)
          x = self.conv3(x)
          x = torch.relu(x)
          x = torch.max_pool2d(x, 2, 2)
          x = x.view(x.size(0), -1)
          x = self.fc1(x)
          x = torch.relu(x)
          return self.fc2(x)
  
  model = simplecnn()
  input_shape = (1, 3, 224, 224)
  img = visualtorch.layered_view(model, input_shape=input_shape, draw_volume=False)
  plt.axis("off")
  plt.tight_layout()
  plt.imshow(img)
  plt.show()
  

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TensorBoardX

• 功能：用于可视化PyTorch模型的指标和图。
• 安装：pip install tensorboardX
• 使用示例：

  from tensorboardX import SummaryWriter
  import torch
  
  writer = SummaryWriter('runs/experiment-1')
  model = ...  # your model here
  images, labels = ...  # your data here
  outputs = model(images)
  loss = ...  # compute loss
  
  writer.add_graph(model, images)
  writer.add_scalar('Loss/train', loss, epoch)
  writer.close()
  

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Netron

• 功能：用于查看和可视化PyTorch模型。
• 安装：pip install netron
• 使用示例：

  import netron
  netron.show('path_to_your_model.pt')
  

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这些工具可以帮助您更好地理解和调试PyTorch模型，选择合适的工具可以根据您的具体需求来决定。

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