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Pytorch中获取模型摘要的3种方法

在pytorch中获取模型的可训练和不可训练的参数,层名称,内核大小和数量。

Pytorch nn.Module 类中没有提供像与Keras那样的可以计算模型中可训练和不可训练的参数的数量并显示模型摘要的方法 。所以在这篇文章中,我将总结我知道三种方法来计算Pytorch模型中可训练和不可训练的参数的数量。

直接手写代码

最直接的办法就是我们自己手写代码代码实现这个功能,所以这里我自己实现了一个函数,函数中为了漂亮所以引入了PrettyTable的包

 from prettytable import PrettyTable
 
 def count_parameters(model):
     table = PrettyTable([“Modules”, “Parameters”])
     total_params = 0
     for name, parameter in model.named_parameters():
         if not parameter.requires_grad: continue
         params = parameter.numel()
         table.add_row([name, params])
         total_params+=params
     print(table)
     print(f”Total Trainable Params: {total_params}”)
     return total_params

我们拿RESNET18为例,以上函数的输出如下:

 +------------------------------+------------+ 
 |           Modules            | Parameters | 
 +------------------------------+------------+ 
 |         conv1.weight         |    9408    | 
 |          bn1.weight          |     64     | 
 |           bn1.bias           |     64     | 
 |    layer1.0.conv1.weight     |   36864    | 
 |     layer1.0.bn1.weight      |     64     | 
 |      layer1.0.bn1.bias       |     64     |
 .
 .
 .
 |          fc.weight           |   512000   | 
 |           fc.bias            |    1000    | 
 +------------------------------+------------+ 
 Total Trainable Params: 11689512

输出以参数为单位,可以看到模型中存在的每个参数的可训练参数,是不是和keras的基本一样。

torchsummary

torchsummary出现的时候的目标就是为了让torch有类似keras一样的打印模型参数的功能,它非常友好并且十分简单。当前版本为1.5.1,可以直接使用pip安装:

 pip install torchsummary

安装完成后即可使用,我们还是以resnet18为例

 from torchsummary import summary
 model = torchvision.models.resnet18().cuda()

在使用时,我们需要生成一个模型的输入变量,也就是模拟模型的前向传播的过程:

 summary(model, input_size = (3, 64, 64), batch_size = -1)

结果如下:

 — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — 
 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
 .
 .
 .
 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 
 ----------------------------------------------------------------

现在,如果你的基本模型有多个分支,每个分支都有不同的输入,例如

 class Model(torch.nn.Module):
     def __init__(self):
         super().__init__()
         self.resnet1 = torchvision.models.resnet18().cuda()
         self.resnet2 = torchvision.models.resnet18().cuda()
         self.resnet3 = torchvision.models.resnet18().cuda()
     
     def forward(self, *x):
         out1 = self.resnet1(x[0])
         out2 = self.resnet2(x[1])
         out3 = self.resnet3(x[2])
         out = torch.cat([out1, out2, out3], dim = 0)
         return out

那么就需要这样:

 summary(Model().cuda(), input_size = [(3, 64, 64)]*3)

该输出将与前一个相似,但会有点混乱,因为torchsummary将每个组成的ResNet模块的信息压缩到一个摘要中,而在两个连续模块的摘要之间没有任何适当的可区分边界。

torchinfo

它看起来可能与torchsummary类似。但在我看来,它是我找到这三种方法中最好的。torchinfo当前版本是1.7.0,还是可以使用pip安装:

 pip install torchinfo

这个包也有一个名为summary的函数。但它有更多的参数。他的使用参数为model (nn.module)、input_size (Sequence of Sizes)、input_data (Sequence of Tensors)、batch_dim (int)、cache_forward_pass (bool)、col_names (Iterable[str])、col_width (int)、depth (int)、device (torch.Device)、dtypes (List[torch.dtype])、mode (str)、row_settings (Iterable[str])、verbose (int)和**kwargs。

参数很多,但是可以直接通过(" input_size ", " output_size ", " num_params ", " kernel_size ", " mult_add ", " trainable ")作为col_names参数来获取信息。

 import torchinfo
 torchinfo.summary(model, (3, 224, 224), batch_dim = 0, col_names = (“input_size”, “output_size”, “num_params”, “kernel_size”, “mult_adds”), verbose = 0)

需要说明的是,如果不使用Jupyter或Google Colab,需要将verbose 更改为1。

上述代码段的输出看起来像这样

 =============================================================================================
 Layer (type:depth-idx)                   Input Shape               Output Shape              Param #                   Kernel Shape              Mult-Adds
 =============================================================================================
 ResNet                                   [1, 3, 224, 224]          [1, 1000]                 --                        --                        --
 ├─Conv2d: 1-1                            [1, 3, 224, 224]          [1, 64, 112, 112]         9,408                     [7, 7]                    118,013,952
 ├─BatchNorm2d: 1-2                       [1, 64, 112, 112]         [1, 64, 112, 112]         128                       --                        128
 ├─ReLU: 1-3                              [1, 64, 112, 112]         [1, 64, 112, 112]         --                        --                        --
 ├─MaxPool2d: 1-4                         [1, 64, 112, 112]         [1, 64, 56, 56]           --                        3                         --
 ├─Sequential: 1-5                        [1, 64, 56, 56]           [1, 64, 56, 56]           --                        --                        --
 │    └─BasicBlock: 2-1                   [1, 64, 56, 56]           [1, 64, 56, 56]           --                        --                        --
 │    │    └─Conv2d: 3-1                  [1, 64, 56, 56]           [1, 64, 56, 56]           36,864                    [3, 3]                    115,605,504
 │    │    └─BatchNorm2d: 3-2             [1, 64, 56, 56]           [1, 64, 56, 56]           128                       --                        128
 │    │    └─ReLU: 3-3                    [1, 64, 56, 56]           [1, 64, 56, 56]           --                        --                        --
 │    │    └─Conv2d: 3-4                  [1, 64, 56, 56]           [1, 64, 56, 56]           36,864                    [3, 3]                    115,605,504
 │    │    └─BatchNorm2d: 3-5             [1, 64, 56, 56]           [1, 64, 56, 56]           128                       --                        128
 │    │    └─ReLU: 3-6                    [1, 64, 56, 56]           [1, 64, 56, 56]           --                        --                        --
 │    └─BasicBlock: 2-2                   [1, 64, 56, 56]           [1, 64, 56, 56]           --                        --                        --
 │    │    └─Conv2d: 3-7                  [1, 64, 56, 56]           [1, 64, 56, 56]           36,864                    [3, 3]                    115,605,504
 │    │    └─BatchNorm2d: 3-8             [1, 64, 56, 56]           [1, 64, 56, 56]           128                       --                        128
 │    │    └─ReLU: 3-9                    [1, 64, 56, 56]           [1, 64, 56, 56]           --                        --                        --
 │    │    └─Conv2d: 3-10                 [1, 64, 56, 56]           [1, 64, 56, 56]           36,864                    [3, 3]                    115,605,504
 │    │    └─BatchNorm2d: 3-11            [1, 64, 56, 56]           [1, 64, 56, 56]           128                       --                        128
 │    │    └─ReLU: 3-12                   [1, 64, 56, 56]           [1, 64, 56, 56]           --                        --                        --
 ├─Sequential: 1-6                        [1, 64, 56, 56]           [1, 128, 28, 28]          --                        --                        --
 │    └─BasicBlock: 2-3                   [1, 64, 56, 56]           [1, 128, 28, 28]          --                        --                        --
 │    │    └─Conv2d: 3-13                 [1, 64, 56, 56]           [1, 128, 28, 28]          73,728                    [3, 3]                    57,802,752
 │    │    └─BatchNorm2d: 3-14            [1, 128, 28, 28]          [1, 128, 28, 28]          256                       --                        256
 .
 .
 .
 │    │    └─Conv2d: 3-49                 [1, 512, 7, 7]            [1, 512, 7, 7]            2,359,296                 [3, 3]                    115,605,504
 │    │    └─BatchNorm2d: 3-50            [1, 512, 7, 7]            [1, 512, 7, 7]            1,024                     --                        1,024
 │    │    └─ReLU: 3-51                   [1, 512, 7, 7]            [1, 512, 7, 7]            --                        --                        --
 ├─AdaptiveAvgPool2d: 1-9                 [1, 512, 7, 7]            [1, 512, 1, 1]            --                        --                        --
 ├─Linear: 1-10                           [1, 512]                  [1, 1000]                 513,000                   --                        513,000
 =============================================================================================
 Total params: 11,689,512
 Trainable params: 11,689,512
 Non-trainable params: 0
 Total mult-adds (G): 1.81
 =============================================================================================
 Input size (MB): 0.60
 Forward/backward pass size (MB): 39.75
 Params size (MB): 46.76
 Estimated Total Size (MB): 87.11
 =============================================================================================

再继续查看多分支模型

 torchinfo.summary(Model().cuda(), [(3, 64, 64)]*3, batch_dim = 0, col_names = (“input_size”, “output_size”, “num_params”, “kernel_size”, “mult_adds”), verbose = 0)

产生以下输出

 =============================================================================================
 Layer (type:depth-idx)                        Input Shape               Output Shape              Param #                   Kernel Shape              Mult-Adds
 =============================================================================================
 Model                                         [1, 3, 64, 64]            [1, 1000]                 --                        --                        --
 ├─ResNet: 1-1                                 [1, 3, 64, 64]            [1, 1000]                 --                        --                        --
 │    └─Conv2d: 2-1                            [1, 3, 64, 64]            [1, 64, 32, 32]           9,408                     [7, 7]                    9,633,792
 │    └─BatchNorm2d: 2-2                       [1, 64, 32, 32]           [1, 64, 32, 32]           128                       --                        128
 │    └─ReLU: 2-3                              [1, 64, 32, 32]           [1, 64, 32, 32]           --                        --                        --
 │    └─MaxPool2d: 2-4                         [1, 64, 32, 32]           [1, 64, 16, 16]           --                        3                         --
 │    └─Sequential: 2-5                        [1, 64, 16, 16]           [1, 64, 16, 16]           --                        --                        --
 │    │    └─BasicBlock: 3-1                   [1, 64, 16, 16]           [1, 64, 16, 16]           73,984                    --                        18,874,624
 │    │    └─BasicBlock: 3-2                   [1, 64, 16, 16]           [1, 64, 16, 16]           73,984                    --                        18,874,624
 │    └─Sequential: 2-6                        [1, 64, 16, 16]           [1, 128, 8, 8]            --                        --                        --
 │    │    └─BasicBlock: 3-3                   [1, 64, 16, 16]           [1, 128, 8, 8]            230,144                   --                        14,680,832
 │    │    └─BasicBlock: 3-4                   [1, 128, 8, 8]            [1, 128, 8, 8]            295,424                   --                        18,874,880
 │    └─Sequential: 2-7                        [1, 128, 8, 8]            [1, 256, 4, 4]            --                        --                        --
 │    │    └─BasicBlock: 3-5                   [1, 128, 8, 8]            [1, 256, 4, 4]            919,040                   --                        14,681,600
 │    │    └─BasicBlock: 3-6                   [1, 256, 4, 4]            [1, 256, 4, 4]            1,180,672                 --                        18,875,392
 │    └─Sequential: 2-8                        [1, 256, 4, 4]            [1, 512, 2, 2]            --                        --                        --
 │    │    └─BasicBlock: 3-7                   [1, 256, 4, 4]            [1, 512, 2, 2]            3,673,088                 --                        14,683,136
 │    │    └─BasicBlock: 3-8                   [1, 512, 2, 2]            [1, 512, 2, 2]            4,720,640                 --                        18,876,416
 │    └─AdaptiveAvgPool2d: 2-9                 [1, 512, 2, 2]            [1, 512, 1, 1]            --                        --                        --
 │    └─Linear: 2-10                           [1, 512]                  [1, 1000]                 513,000                   --                        513,000
 ├─ResNet: 1-2                                 [1, 3, 64, 64]            [1, 1000]                 --                        --                        --
 │    └─Conv2d: 2-11                           [1, 3, 64, 64]            [1, 64, 32, 32]           9,408                     [7, 7]                    9,633,792
 │    └─BatchNorm2d: 2-12                      [1, 64, 32, 32]           [1, 64, 32, 32]           128                       --                        128
 │    └─ReLU: 2-13                             [1, 64, 32, 32]           [1, 64, 32, 32]           --                        --                        --
 │    └─MaxPool2d: 2-14                        [1, 64, 32, 32]           [1, 64, 16, 16]           --                        3                         --
 │    └─Sequential: 2-15                       [1, 64, 16, 16]           [1, 64, 16, 16]           --                        --                        --
 │    │    └─BasicBlock: 3-9                   [1, 64, 16, 16]           [1, 64, 16, 16]           73,984                    --                        18,874,624
 │    │    └─BasicBlock: 3-10                  [1, 64, 16, 16]           [1, 64, 16, 16]           73,984                    --                        18,874,624
 │    └─Sequential: 2-16                       [1, 64, 16, 16]           [1, 128, 8, 8]            --                        --                        --
 │    │    └─BasicBlock: 3-11                  [1, 64, 16, 16]           [1, 128, 8, 8]            230,144                   --                        14,680,832
 │    │    └─BasicBlock: 3-12                  [1, 128, 8, 8]            [1, 128, 8, 8]            295,424                   --                        18,874,880
 │    └─Sequential: 2-17                       [1, 128, 8, 8]            [1, 256, 4, 4]            --                        --                        --
 │    │    └─BasicBlock: 3-13                  [1, 128, 8, 8]            [1, 256, 4, 4]            919,040                   --                        14,681,600
 │    │    └─BasicBlock: 3-14                  [1, 256, 4, 4]            [1, 256, 4, 4]            1,180,672                 --                        18,875,392
 │    └─Sequential: 2-18                       [1, 256, 4, 4]            [1, 512, 2, 2]            --                        --                        --
 │    │    └─BasicBlock: 3-15                  [1, 256, 4, 4]            [1, 512, 2, 2]            3,673,088                 --                        14,683,136
 │    │    └─BasicBlock: 3-16                  [1, 512, 2, 2]            [1, 512, 2, 2]            4,720,640                 --                        18,876,416
 │    └─AdaptiveAvgPool2d: 2-19                [1, 512, 2, 2]            [1, 512, 1, 1]            --                        --                        --
 │    └─Linear: 2-20                           [1, 512]                  [1, 1000]                 513,000                   --                        513,000
 ├─ResNet: 1-3                                 [1, 3, 64, 64]            [1, 1000]                 --                        --                        --
 │    └─Conv2d: 2-21                           [1, 3, 64, 64]            [1, 64, 32, 32]           9,408                     [7, 7]                    9,633,792
 │    └─BatchNorm2d: 2-22                      [1, 64, 32, 32]           [1, 64, 32, 32]           128                       --                        128
 │    └─ReLU: 2-23                             [1, 64, 32, 32]           [1, 64, 32, 32]           --                        --                        --
 │    └─MaxPool2d: 2-24                        [1, 64, 32, 32]           [1, 64, 16, 16]           --                        3                         --
 │    └─Sequential: 2-25                       [1, 64, 16, 16]           [1, 64, 16, 16]           --                        --                        --
 │    │    └─BasicBlock: 3-17                  [1, 64, 16, 16]           [1, 64, 16, 16]           73,984                    --                        18,874,624
 │    │    └─BasicBlock: 3-18                  [1, 64, 16, 16]           [1, 64, 16, 16]           73,984                    --                        18,874,624
 │    └─Sequential: 2-26                       [1, 64, 16, 16]           [1, 128, 8, 8]            --                        --                        --
 │    │    └─BasicBlock: 3-19                  [1, 64, 16, 16]           [1, 128, 8, 8]            230,144                   --                        14,680,832
 │    │    └─BasicBlock: 3-20                  [1, 128, 8, 8]            [1, 128, 8, 8]            295,424                   --                        18,874,880
 │    └─Sequential: 2-27                       [1, 128, 8, 8]            [1, 256, 4, 4]            --                        --                        --
 │    │    └─BasicBlock: 3-21                  [1, 128, 8, 8]            [1, 256, 4, 4]            919,040                   --                        14,681,600
 │    │    └─BasicBlock: 3-22                  [1, 256, 4, 4]            [1, 256, 4, 4]            1,180,672                 --                        18,875,392
 │    └─Sequential: 2-28                       [1, 256, 4, 4]            [1, 512, 2, 2]            --                        --                        --
 │    │    └─BasicBlock: 3-23                  [1, 256, 4, 4]            [1, 512, 2, 2]            3,673,088                 --                        14,683,136
 │    │    └─BasicBlock: 3-24                  [1, 512, 2, 2]            [1, 512, 2, 2]            4,720,640                 --                        18,876,416
 │    └─AdaptiveAvgPool2d: 2-29                [1, 512, 2, 2]            [1, 512, 1, 1]            --                        --                        --
 │    └─Linear: 2-30                           [1, 512]                  [1, 1000]                 513,000                   --                        513,000
 =============================================================================================
 Total params: 35,068,536
 Trainable params: 35,068,536
 Non-trainable params: 0
 Total mult-adds (M): 445.71
 =============================================================================================
 Input size (MB): 0.15
 Forward/backward pass size (MB): 9.76
 Params size (MB): 140.27
 Estimated Total Size (MB): 150.18
 =============================================================================================

可以看到depth 参数的默认值为3。并且在可视化方向上,多分支被重新进行了组织并且以层次结构方式呈现,所以很容易区分,所以他的效果要比torchsummary好很多。

作者:Siladittya Manna

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