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Pytorch - 使用torchsummary/torchsummaryX/torchinfo库打印模型结构、输出维度和参数信息
1 torchsummary/torchsummaryX
torchsummary Github地址：https://github.com/sksq96/pytorch-summary
torchsummaryX Github地址：https://github.com/nmhkahn/torchsummaryX
torchinfo Github地址：https://github.com/TylerYep/torchinfo
1.1 安装
安装torchsummary
pip install torchsummary- 1
安装torchsummaryX
pip install torchsummaryX- 1
安装torchinfo
pippip install torchinfo- 1
conda
conda install -c conda-forge torchinfo- 1
1.2 使用
1.2.1 torchsummary的使用
from torchvision import models from torchsummary import summary if __name__ == '__main__': resnet18 = models.resnet18().cuda() # 不加.cuda()会报错 summary(resnet18, (3, 224, 224))- 1
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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] 0 AdaptiveAvgPool2d-67 [-1, 512, 1, 1] 0 Linear-68 [-1, 1000] 513,000 ================================================================ Total params: 11,689,512 Trainable params: 11,689,512 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.57 Forward/backward pass size (MB): 62.79 Params size (MB): 44.59 Estimated Total Size (MB): 107.96 ----------------------------------------------------------------- 1
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1.2.2 torchsummaryX的使用
import torch import torch.nn as nn from torchsummaryX import summary class Net(nn.Module): def __init__(self, vocab_size=20, embed_dim=300, hidden_dim=512, num_layers=2): super().__init__() self.hidden_dim = hidden_dim self.embedding = nn.Embedding(vocab_size, embed_dim) self.encoder = nn.LSTM(embed_dim, hidden_dim, num_layers=num_layers) self.decoder = nn.Linear(hidden_dim, vocab_size) def forward(self, x): embed = self.embedding(x) out, hidden = self.encoder(embed) out = self.decoder(out) out = out.view(-1, out.size(2)) return out, hidden if __name__ == '__main__': inputs = torch.zeros((100, 1), dtype=torch.long) # [length, batch_size] summary(Net(), inputs)- 1
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输出
=========================================================== Kernel Shape Output Shape Params Mult-Adds Layer 0_embedding [300, 20] [100, 1, 300] 6000 6000 1_encoder - [100, 1, 512] 3768320 3760128 2_decoder [512, 20] [100, 1, 20] 10260 10240 ----------------------------------------------------------- Totals Total params 3784580 Trainable params 3784580 Non-trainable params 0 Mult-Adds 3776368 ===========================================================- 1
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1.2.3 torchinfo的使用
from torchvision import models from torchinfo import summary if __name__ == '__main__': resnet18 = models.resnet18().cuda() # 不加.cuda()会报错 summary(resnet18, input_size=(1, 3, 244, 244))- 1
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输出
========================================================================================== Layer (type:depth-idx) Output Shape Param # ========================================================================================== ResNet [1, 1000] -- ├─Conv2d: 1-1 [1, 64, 122, 122] 9,408 ├─BatchNorm2d: 1-2 [1, 64, 122, 122] 128 ├─ReLU: 1-3 [1, 64, 122, 122] -- ├─MaxPool2d: 1-4 [1, 64, 61, 61] -- ├─Sequential: 1-5 [1, 64, 61, 61] -- │ └─BasicBlock: 2-1 [1, 64, 61, 61] -- │ │ └─Conv2d: 3-1 [1, 64, 61, 61] 36,864 │ │ └─BatchNorm2d: 3-2 [1, 64, 61, 61] 128 │ │ └─ReLU: 3-3 [1, 64, 61, 61] -- │ │ └─Conv2d: 3-4 [1, 64, 61, 61] 36,864 │ │ └─BatchNorm2d: 3-5 [1, 64, 61, 61] 128 │ │ └─ReLU: 3-6 [1, 64, 61, 61] -- │ └─BasicBlock: 2-2 [1, 64, 61, 61] -- │ │ └─Conv2d: 3-7 [1, 64, 61, 61] 36,864 │ │ └─BatchNorm2d: 3-8 [1, 64, 61, 61] 128 │ │ └─ReLU: 3-9 [1, 64, 61, 61] -- │ │ └─Conv2d: 3-10 [1, 64, 61, 61] 36,864 │ │ └─BatchNorm2d: 3-11 [1, 64, 61, 61] 128 │ │ └─ReLU: 3-12 [1, 64, 61, 61] -- ├─Sequential: 1-6 [1, 128, 31, 31] -- │ └─BasicBlock: 2-3 [1, 128, 31, 31] -- │ │ └─Conv2d: 3-13 [1, 128, 31, 31] 73,728 │ │ └─BatchNorm2d: 3-14 [1, 128, 31, 31] 256 │ │ └─ReLU: 3-15 [1, 128, 31, 31] -- │ │ └─Conv2d: 3-16 [1, 128, 31, 31] 147,456 │ │ └─BatchNorm2d: 3-17 [1, 128, 31, 31] 256 │ │ └─Sequential: 3-18 [1, 128, 31, 31] 8,448 │ │ └─ReLU: 3-19 [1, 128, 31, 31] -- │ └─BasicBlock: 2-4 [1, 128, 31, 31] -- │ │ └─Conv2d: 3-20 [1, 128, 31, 31] 147,456 │ │ └─BatchNorm2d: 3-21 [1, 128, 31, 31] 256 │ │ └─ReLU: 3-22 [1, 128, 31, 31] -- │ │ └─Conv2d: 3-23 [1, 128, 31, 31] 147,456 │ │ └─BatchNorm2d: 3-24 [1, 128, 31, 31] 256 │ │ └─ReLU: 3-25 [1, 128, 31, 31] -- ├─Sequential: 1-7 [1, 256, 16, 16] -- │ └─BasicBlock: 2-5 [1, 256, 16, 16] -- │ │ └─Conv2d: 3-26 [1, 256, 16, 16] 294,912 │ │ └─BatchNorm2d: 3-27 [1, 256, 16, 16] 512 │ │ └─ReLU: 3-28 [1, 256, 16, 16] -- │ │ └─Conv2d: 3-29 [1, 256, 16, 16] 589,824 │ │ └─BatchNorm2d: 3-30 [1, 256, 16, 16] 512 │ │ └─Sequential: 3-31 [1, 256, 16, 16] 33,280 │ │ └─ReLU: 3-32 [1, 256, 16, 16] -- │ └─BasicBlock: 2-6 [1, 256, 16, 16] -- │ │ └─Conv2d: 3-33 [1, 256, 16, 16] 589,824 │ │ └─BatchNorm2d: 3-34 [1, 256, 16, 16] 512 │ │ └─ReLU: 3-35 [1, 256, 16, 16] -- │ │ └─Conv2d: 3-36 [1, 256, 16, 16] 589,824 │ │ └─BatchNorm2d: 3-37 [1, 256, 16, 16] 512 │ │ └─ReLU: 3-38 [1, 256, 16, 16] -- ├─Sequential: 1-8 [1, 512, 8, 8] -- │ └─BasicBlock: 2-7 [1, 512, 8, 8] -- │ │ └─Conv2d: 3-39 [1, 512, 8, 8] 1,179,648 │ │ └─BatchNorm2d: 3-40 [1, 512, 8, 8] 1,024 │ │ └─ReLU: 3-41 [1, 512, 8, 8] -- │ │ └─Conv2d: 3-42 [1, 512, 8, 8] 2,359,296 │ │ └─BatchNorm2d: 3-43 [1, 512, 8, 8] 1,024 │ │ └─Sequential: 3-44 [1, 512, 8, 8] 132,096 │ │ └─ReLU: 3-45 [1, 512, 8, 8] -- │ └─BasicBlock: 2-8 [1, 512, 8, 8] -- │ │ └─Conv2d: 3-46 [1, 512, 8, 8] 2,359,296 │ │ └─BatchNorm2d: 3-47 [1, 512, 8, 8] 1,024 │ │ └─ReLU: 3-48 [1, 512, 8, 8] -- │ │ └─Conv2d: 3-49 [1, 512, 8, 8] 2,359,296 │ │ └─BatchNorm2d: 3-50 [1, 512, 8, 8] 1,024 │ │ └─ReLU: 3-51 [1, 512, 8, 8] -- ├─AdaptiveAvgPool2d: 1-9 [1, 512, 1, 1] -- ├─Linear: 1-10 [1, 1000] 513,000 ========================================================================================== Total params: 11,689,512 Trainable params: 11,689,512 Non-trainable params: 0 Total mult-adds (G): 2.27 ========================================================================================== Input size (MB): 0.71 Forward/backward pass size (MB): 48.20 Params size (MB): 46.76 Estimated Total Size (MB): 95.67 ==========================================================================================- 1
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- 原文地址：https://blog.csdn.net/HW140701/article/details/125554060
