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引言
使用 PyTorch 深度学习搭建模型后,如果想查看模型结构,可以直接使用 print(model)
函数打印。但该输出结果不是特别直观,今天给大家推荐一个类似 keras 风格 model.summary()
的模型可视化工具。
安装
pip install torchsummary
用法
- 示例
from torchvision import modelsfrom torchsummary import summaryresnet18 = models.resnet18().cuda() # 不加.cuda()会报错summary(resnet18, (3, 224, 224))
- 输出
---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 112, 112] 9,408 BatchNorm2d-2 [-1, 64, 112, 112] 128 ReLU-3 [-1, 64, 112, 112] 0 MaxPool2d-4 [-1, 64, 56, 56] 0 Conv2d-5 [-1, 64, 56, 56] 36,864 BatchNorm2d-6 [-1, 64, 56, 56] 128 ReLU-7 [-1, 64, 56, 56] 0 Conv2d-8 [-1, 64, 56, 56] 36,864 BatchNorm2d-9 [-1, 64, 56, 56] 128 ReLU-10 [-1, 64, 56, 56] 0 BasicBlock-11 [-1, 64, 56, 56] 0 Conv2d-12 [-1, 64, 56, 56] 36,864 BatchNorm2d-13 [-1, 64, 56, 56] 128 ReLU-14 [-1, 64, 56, 56] 0 Conv2d-15 [-1, 64, 56, 56] 36,864 BatchNorm2d-16 [-1, 64, 56, 56] 128 ReLU-17 [-1, 64, 56, 56] 0 BasicBlock-18 [-1, 64, 56, 56] 0 Conv2d-19 [-1, 128, 28, 28] 73,728 BatchNorm2d-20 [-1, 128, 28, 28] 256 ReLU-21 [-1, 128, 28, 28] 0 Conv2d-22 [-1, 128, 28, 28] 147,456 BatchNorm2d-23 [-1, 128, 28, 28] 256 Conv2d-24 [-1, 128, 28, 28] 8,192 BatchNorm2d-25 [-1, 128, 28, 28] 256 ReLU-26 [-1, 128, 28, 28] 0 BasicBlock-27 [-1, 128, 28, 28] 0 Conv2d-28 [-1, 128, 28, 28] 147,456 BatchNorm2d-29 [-1, 128, 28, 28] 256 ReLU-30 [-1, 128, 28, 28] 0 Conv2d-31 [-1, 128, 28, 28] 147,456 BatchNorm2d-32 [-1, 128, 28, 28] 256 ReLU-33 [-1, 128, 28, 28] 0 BasicBlock-34 [-1, 128, 28, 28] 0 Conv2d-35 [-1, 256, 14, 14] 294,912 BatchNorm2d-36 [-1, 256, 14, 14] 512 ReLU-37 [-1, 256, 14, 14] 0 Conv2d-38 [-1, 256, 14, 14] 589,824 BatchNorm2d-39 [-1, 256, 14, 14] 512 Conv2d-40 [-1, 256, 14, 14] 32,768 BatchNorm2d-41 [-1, 256, 14, 14] 512 ReLU-42 [-1, 256, 14, 14] 0 BasicBlock-43 [-1, 256, 14, 14] 0 Conv2d-44 [-1, 256, 14, 14] 589,824 BatchNorm2d-45 [-1, 256, 14, 14] 512 ReLU-46 [-1, 256, 14, 14] 0 Conv2d-47 [-1, 256, 14, 14] 589,824 BatchNorm2d-48 [-1, 256, 14, 14] 512 ReLU-49 [-1, 256, 14, 14] 0 BasicBlock-50 [-1, 256, 14, 14] 0 Conv2d-51 [-1, 512, 7, 7] 1,179,648 BatchNorm2d-52 [-1, 512, 7, 7] 1,024 ReLU-53 [-1, 512, 7, 7] 0 Conv2d-54 [-1, 512, 7, 7] 2,359,296 BatchNorm2d-55 [-1, 512, 7, 7] 1,024 Conv2d-56 [-1, 512, 7, 7] 131,072 BatchNorm2d-57 [-1, 512, 7, 7] 1,024 ReLU-58 [-1, 512, 7, 7] 0 BasicBlock-59 [-1, 512, 7, 7] 0 Conv2d-60 [-1, 512, 7, 7] 2,359,296 BatchNorm2d-61 [-1, 512, 7, 7] 1,024 ReLU-62 [-1, 512, 7, 7] 0 Conv2d-63 [-1, 512, 7, 7] 2,359,296 BatchNorm2d-64 [-1, 512, 7, 7] 1,024 ReLU-65 [-1, 512, 7, 7] 0 BasicBlock-66 [-1, 512, 7, 7] 0AdaptiveAvgPool2d-67 [-1, 512, 1, 1] 0 Linear-68 [-1, 1000] 513,000================================================================Total params: 11,689,512Trainable params: 11,689,512Non-trainable params: 0----------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 62.79Params size (MB): 44.59Estimated Total Size (MB): 107.96----------------------------------------------------------------
- 存疑
Resnet18 明明是 17 个卷积层加 1 个全连接层,为什么输出中打印出了 20 个卷积层??
- 也就是说多了 3 个卷积层,我们先打印模型结构
print(resnet18)
看一下
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, e 以上是关于PyTorch打印模型结构输出维度和参数信息(torchsummary)的主要内容,如果未能解决你的问题,请参考以下文章