Python如何加载模型并查看网络

加载模型并查看网络在Python中是非常常见的操作，一般可以通过以下步骤完成：

1. 加载模型

在Python中加载模型，可以使用torch.load()函数从文件中读取保存的模型，语法如下：

import torch

# Load the trained model
model = torch.load("path/to/model.pth")

其中，path/to/model.pth是保存的模型文件的路径，可以是绝对路径或相对路径。

2. 查看网络结构

我们可以使用print()函数或tensorboard来查看网络结构。如果要使用print()函数，需要遍历模型的所有模块并打印它们的结构，示例代码如下：

import torch
import torchvision.models as models

# Load the pre-trained ResNet18
resnet18 = models.resnet18(pretrained=True)

# Print the network structure
for name, module in resnet18.named_modules():
    print(name, module)

其中，models.resnet18(pretrained=True)是载入已经预训练好的ResNet18模型，resnet18.named_modules()返回模型中所有包含在模块列表中的子模块以及它们的名字。通过遍历模型的子模块来打印它们的名字和结构。

另外，我们也可以使用tensorboard查看网络结构，示例代码如下：

import torch
from tensorboardX import SummaryWriter

# Load the trained model
model = torch.load("path/to/model.pth")

# Export to TensorBoard
writer = SummaryWriter()
writer.add_graph(model)
writer.close()

其中，SummaryWriter()用于创建SummaryWriter对象来将数据写入TensorBoard，writer.add_graph(model)则将模型输出到TensorBoard中。我们可以通过浏览器打开TensorBoard来查看网络结构。

举例如下：

1. 示例一：加载已经训练好的模型，并输出网络结构。

import torch
import torchvision.models as models

# Load the pre-trained ResNet18
resnet18 = models.resnet18(pretrained=True)

# Print the network structure
for name, module in resnet18.named_modules():
    print(name, module)

输出结果如下：

conv1 Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
bn1 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
relu ReLU(inplace=True)
maxpool MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
layer1.0.conv1 Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer1.0.bn1 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer1.0.relu ReLU(inplace=True)
layer1.0.conv2 Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer1.0.bn2 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer1.1.conv1 Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer1.1.bn1 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer1.1.relu ReLU(inplace=True)
layer1.1.conv2 Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer1.1.bn2 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer2.0.conv1 Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
layer2.0.bn1 BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer2.0.relu ReLU(inplace=True)
layer2.0.conv2 Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer2.0.bn2 BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer2.0.downsample Sequential(
  (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
  (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
layer2.1.conv1 Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer2.1.bn1 BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer2.1.relu ReLU(inplace=True)
layer2.1.conv2 Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer2.1.bn2 BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer3.0.conv1 Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
layer3.0.bn1 BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer3.0.relu ReLU(inplace=True)
layer3.0.conv2 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer3.0.bn2 BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer3.0.downsample Sequential(
  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
layer3.1.conv1 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer3.1.bn1 BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer3.1.relu ReLU(inplace=True)
layer3.1.conv2 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer3.1.bn2 BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer4.0.conv1 Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
layer4.0.bn1 BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer4.0.relu ReLU(inplace=True)
layer4.0.conv2 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer4.0.bn2 BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer4.0.downsample Sequential(
  (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
  (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
layer4.1.conv1 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer4.1.bn1 BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
layer4.1.relu ReLU(inplace=True)
layer4.1.conv2 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
layer4.1.bn2 BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
avgpool AdaptiveAvgPool2d(output_size=(1, 1))
fc Linear(in_features=512, out_features=1000, bias=True)

1. 示例二：使用tensorboard输出网络结构。

import torch
from tensorboardX import SummaryWriter

# Load the trained model
model = torch.load("path/to/model.pth")

# Export to TensorBoard
writer = SummaryWriter()
writer.add_graph(model)
writer.close()

请确保将path/to/model.pth替换成实际的模型文件路径。运行上述代码后，打开TensorBoard界面，点击Graph选项卡，即可看到模型的图表结构。