在 PyTorch 中，named_children、named_modules 和 named_parameters 是用于获取神经网络模型组件和参数的三种不同的方法。下面是它们各自的作用和区别：
named_parameters：递归地列出所有参数名称和tensornamed_modules：递归地列出所有子层，其中第一个返回值就是模型本身named_children：列出模型的第一层级的子层，不往下进行深入递归
1. named_children:
named_children返回一个生成器，它包含模型中所有直接子模块的名称和模块对。- 它只返回一层级的子模块，不递归到更深层次的子模块。
- 这个方法通常用于迭代模型的直接子模块，并对其进行操作或检查。
示例:
from torchvision.models import resnet18
model = resnet18()
# Print each layer name and its module
# Note that named_children method only returns the first level submodules
for name, layer in model.named_children():
print(name.ljust(10), '-->', type(layer))
输出：
conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
relu --> <class 'torch.nn.modules.activation.ReLU'>
maxpool --> <class 'torch.nn.modules.pooling.MaxPool2d'>
layer1 --> <class 'torch.nn.modules.container.Sequential'>
layer2 --> <class 'torch.nn.modules.container.Sequential'>
layer3 --> <class 'torch.nn.modules.container.Sequential'>
layer4 --> <class 'torch.nn.modules.container.Sequential'>
avgpool --> <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>
fc --> <class 'torch.nn.modules.linear.Linear'>
2. named_modules:
named_modules返回一个生成器，它包含模型中所有模块的名称和模块对，包括子模块的子模块。- 它递归地遍历整个模型，返回所有模块的名称和引用。
- 这个方法适用于当你需要对模型中的所有模块进行操作或检查时，无论它们位于哪一层级。
示例:
from torchvision.models import resnet18
model = resnet18()
# The first layer is the model itself
for name, layer in model.named_modules():
print(name.ljust(15), '-->', type(layer))
输出：
--> <class 'torchvision.models.resnet.ResNet'>
conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
relu --> <class 'torch.nn.modules.activation.ReLU'>
maxpool --> <class 'torch.nn.modules.pooling.MaxPool2d'>
layer1 --> <class 'torch.nn.modules.container.Sequential'>
layer1.0 --> <class 'torchvision.models.resnet.BasicBlock'>
layer1.0.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer1.0.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer1.0.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer1.0.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer1.0.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer1.1 --> <class 'torchvision.models.resnet.BasicBlock'>
layer1.1.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer1.1.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer1.1.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer1.1.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer1.1.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer2 --> <class 'torch.nn.modules.container.Sequential'>
layer2.0 --> <class 'torchvision.models.resnet.BasicBlock'>
layer2.0.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer2.0.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer2.0.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer2.0.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer2.0.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer2.0.downsample --> <class 'torch.nn.modules.container.Sequential'>
layer2.0.downsample.0 --> <class 'torch.nn.modules.conv.Conv2d'>
layer2.0.downsample.1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer2.1 --> <class 'torchvision.models.resnet.BasicBlock'>
layer2.1.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer2.1.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer2.1.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer2.1.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer2.1.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer3 --> <class 'torch.nn.modules.container.Sequential'>
layer3.0 --> <class 'torchvision.models.resnet.BasicBlock'>
layer3.0.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer3.0.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer3.0.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer3.0.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer3.0.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer3.0.downsample --> <class 'torch.nn.modules.container.Sequential'>
layer3.0.downsample.0 --> <class 'torch.nn.modules.conv.Conv2d'>
layer3.0.downsample.1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer3.1 --> <class 'torchvision.models.resnet.BasicBlock'>
layer3.1.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer3.1.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer3.1.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer3.1.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer3.1.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer4 --> <class 'torch.nn.modules.container.Sequential'>
layer4.0 --> <class 'torchvision.models.resnet.BasicBlock'>
layer4.0.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer4.0.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer4.0.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer4.0.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer4.0.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer4.0.downsample --> <class 'torch.nn.modules.container.Sequential'>
layer4.0.downsample.0 --> <class 'torch.nn.modules.conv.Conv2d'>
layer4.0.downsample.1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer4.1 --> <class 'torchvision.models.resnet.BasicBlock'>
layer4.1.conv1 --> <class 'torch.nn.modules.conv.Conv2d'>
layer4.1.bn1 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
layer4.1.relu --> <class 'torch.nn.modules.activation.ReLU'>
layer4.1.conv2 --> <class 'torch.nn.modules.conv.Conv2d'>
layer4.1.bn2 --> <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
avgpool --> <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>
fc --> <class 'torch.nn.modules.linear.Linear'>
3. named_parameters:
named_parameters返回一个生成器，它包含模型中所有参数的名称和参数值对。- 它递归地遍历模型，返回所有可训练参数的名称和参数张量。
- 这个方法用于获取和检查模型中的参数，例如在打印模型参数、保存模型或加载模型时使用。
示例:
from torchvision.models import resnet18
model = resnet18()
for name, param in model.named_parame
conv1.weight --> torch.Size([64, 3, 7, 7])
bn1.weight --> torch.Size([64])
bn1.bias --> torch.Size([64])
layer1.0.conv1.weight --> torch.Size([64, 64, 3, 3])
layer1.0.bn1.weight --> torch.Size([64])
layer1.0.bn1.bias --> torch.Size([64])
layer1.0.conv2.weight --> torch.Size([64, 64, 3, 3])
layer1.0.bn2.weight --> torch.Size([64])
layer1.0.bn2.bias --> torch.Size([64])
layer1.1.conv1.weight --> torch.Size([64, 64, 3, 3])
layer1.1.bn1.weight --> torch.Size([64])
layer1.1.bn1.bias --> torch.Size([64])
layer1.1.conv2.weight --> torch.Size([64, 64, 3, 3])
layer1.1.bn2.weight --> torch.Size([64])
layer1.1.bn2.bias --> torch.Size([64])
layer2.0.conv1.weight --> torch.Size([128, 64, 3, 3])
layer2.0.bn1.weight --> torch.Size([128])
layer2.0.bn1.bias --> torch.Size([128])
layer2.0.conv2.weight --> torch.Size([128, 128, 3, 3])
layer2.0.bn2.weight --> torch.Size([128])
layer2.0.bn2.bias --> torch.Size([128])
layer2.0.downsample.0.weight --> torch.Size([128, 64, 1, 1])
layer2.0.downsample.1.weight --> torch.Size([128])
layer2.0.downsample.1.bias --> torch.Size([128])
layer2.1.conv1.weight --> torch.Size([128, 128, 3, 3])
layer2.1.bn1.weight --> torch.Size([128])
layer2.1.bn1.bias --> torch.Size([128])
layer2.1.conv2.weight --> torch.Size([128, 128, 3, 3])
layer2.1.bn2.weight --> torch.Size([128])
layer2.1.bn2.bias --> torch.Size([128])
layer3.0.conv1.weight --> torch.Size([256, 128, 3, 3])
layer3.0.bn1.weight --> torch.Size([256])
layer3.0.bn1.bias --> torch.Size([256])
layer3.0.conv2.weight --> torch.Size([256, 256, 3, 3])
layer3.0.bn2.weight --> torch.Size([256])
layer3.0.bn2.bias --> torch.Size([256])
layer3.0.downsample.0.weight --> torch.Size([256, 128, 1, 1])
layer3.0.downsample.1.weight --> torch.Size([256])
layer3.0.downsample.1.bias --> torch.Size([256])
layer3.1.conv1.weight --> torch.Size([256, 256, 3, 3])
layer3.1.bn1.weight --> torch.Size([256])
layer3.1.bn1.bias --> torch.Size([256])
layer3.1.conv2.weight --> torch.Size([256, 256, 3, 3])
layer3.1.bn2.weight --> torch.Size([256])
layer3.1.bn2.bias --> torch.Size([256])
layer4.0.conv1.weight --> torch.Size([512, 256, 3, 3])
layer4.0.bn1.weight --> torch.Size([512])
layer4.0.bn1.bias --> torch.Size([512])
layer4.0.conv2.weight --> torch.Size([512, 512, 3, 3])
layer4.0.bn2.weight --> torch.Size([512])
layer4.0.bn2.bias --> torch.Size([512])
layer4.0.downsample.0.weight --> torch.Size([512, 256, 1, 1])
layer4.0.downsample.1.weight --> torch.Size([512])
layer4.0.downsample.1.bias --> torch.Size([512])
layer4.1.conv1.weight --> torch.Size([512, 512, 3, 3])
layer4.1.bn1.weight --> torch.Size([512])
layer4.1.bn1.bias --> torch.Size([512])
layer4.1.conv2.weight --> torch.Size([512, 512, 3, 3])
layer4.1.bn2.weight --> torch.Size([512])
layer4.1.bn2.bias --> torch.Size([512])
fc.weight --> torch.Size([1000, 512])
fc.bias --> torch.Size([1000])
总结来说，named_children 用于获取模型的直接子模块，named_modules 用于获取模型的所有模块（包括嵌套的子模块），而 named_parameters 用于获取模型中的所有参数。这些方法在模型调试、分析和优化时非常有用。