- print 直接输出网络结构
print(model)
print 只能打印最基本的网络结构，显示每一层的操作，输出结果如下：
Classifier(
(cnn): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU()
(7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(8): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): ReLU()
(11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(12): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(14): ReLU()
(15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(18): ReLU()
(19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(fc): Sequential(
(0): Linear(in_features=8192, out_features=1024, bias=True)
(1): ReLU()
(2): Linear(in_features=1024, out_features=512, bias=True)
(3): ReLU()
(4): Linear(in_features=512, out_features=11, bias=True)
)
)
- from torchsummary import summary
summary(model,(3,128,128))
torchsummary 中的 summary 可以打印每一层的shape， 参数量，输出结果如下：
`
Input size (MB): 0.19
Forward/backward pass size (MB): 49.59
Params size (MB): 48.96
Estimated Total Size (MB): 98.73
Original: https://blog.csdn.net/like_jmo/article/details/126903727
Author: CV小Rookie
Title: Pytorch 中打印网络结构及其参数的方法与实现
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