Pytorch：获得模型每一层的名字

以resnet18模型为例，可以打印出模型的结构：

from torchvision import models
model = models.resnet18()
print(model)

'''打印结果'''
# ResNet(
#   (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): Sequential(
#     (0): BasicBlock(
#       (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#     )
#     (1): BasicBlock(
#       (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#     )
#   )
#   (layer2): Sequential(
#     (0): BasicBlock(
#       (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (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)
#       )
#     )
#     (1): BasicBlock(
#       (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#     )
#   )
#   (layer3): Sequential(
#     (0): BasicBlock(
#       (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (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)
#       )
#     )
#     (1): BasicBlock(
#       (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#     )
#   )
#   (layer4): Sequential(
#     (0): BasicBlock(
#       (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (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)
#       )
#     )
#     (1): BasicBlock(
#       (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#       (relu): ReLU(inplace=True)
#       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
#       (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)
# )

一共10层，如何获得每一层的名称？可以进入model.__repr__()方法（控制print结果的方法），看到每一层括号里的名称是从model._modules.items()获得的，因此打印每一层名称的完整代码如下：

from torchvision import models
model = models.resnet18()
# print(model)
names = [item[0] for item in model._modules.items()]
print(names)

即可打印出[‘conv1’, ‘bn1’, ‘relu’, ‘maxpool’, ‘layer1’, ‘layer2’, ‘layer3’, ‘layer4’, ‘avgpool’, ‘fc’]