torchinfo(以前的torchsummary)包产生类似Keras1的输出(对于给定的输入形状):2

from torchinfo import summary

model = ConvNet()
batch_size = 16
summary(model, input_size=(batch_size, 1, 28, 28))

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Conv2d (conv1): 1-1                    [5, 10, 24, 24]           260
├─Conv2d (conv2): 1-2                    [5, 20, 8, 8]             5,020
├─Dropout2d (conv2_drop): 1-3            [5, 20, 8, 8]             --
├─Linear (fc1): 1-4                      [5, 50]                   16,050
├─Linear (fc2): 1-5                      [5, 10]                   510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 7.69
==========================================================================================
Input size (MB): 0.05
Forward/backward pass size (MB): 0.91
Params size (MB): 0.09
Estimated Total Size (MB): 1.05
==========================================================================================

Notes: Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary()... Unlike Keras, PyTorch has a dynamic computational graph which can adapt to any compatible input shape across multiple calls e.g. any sufficiently large image size (for a fully convolutional network). As such, it cannot present an inherent set of input/output shapes for each layer, as these are input-dependent, and why in the above package you must specify the input dimensions.

Summary (my_model， (3, 224, 224)， device = 'cpu')可以解决这个问题。

我更喜欢这个简单的片段——

net = model
modules = [module for module in net.modules()]
params = [param.shape for param in net.parameters()]

# Print Model Summary
print(modules[0])
total_params=0
for i in range(1,len(modules)):
   j = 2*i
   param = (params[j-2][1]*params[j-2][0])+params[j-1][0]
   total_params += param
   print("Layer",i,"->\t",end="")
   print("Weights:", params[j-2][0],"x",params[j-2][1],
         "\tBias: ",params[j-1][0], "\tParameters: ", param)
print("\nTotal Params: ", total_params)

这能打印出我需要的一切

Net(
  (hLayer1): Linear(in_features=1024, out_features=256, bias=True)
  (hLayer2): Linear(in_features=256, out_features=128, bias=True)
  (hLayer3): Linear(in_features=128, out_features=64, bias=True)
  (outLayer): Linear(in_features=64, out_features=10, bias=True)
)
Layer 1 ->  Weights: 256 x 1024     Bias:  256  Parameters:  262400
Layer 2 ->  Weights: 128 x 256      Bias:  128  Parameters:  32896
Layer 3 ->  Weights: 64 x 128       Bias:  64   Parameters:  8256
Layer 4 ->  Weights: 10 x 64        Bias:  10   Parameters:  650

Total Parameters:  304202

对于复杂的模型或更深入的模型统计

安装火炬

pip install torchstat

获得统计数据

from torchstat import stat
import torchvision.models as models

model = models.vgg19()
stat(model, (3, 224, 224))

输出-

        module name  input shape output shape       params memory(MB)              MAdd             Flops   MemRead(B)  MemWrite(B) duration[%]    MemR+W(B)
0        features.0    3 224 224   64 224 224       1792.0      12.25     173,408,256.0      89,915,392.0     609280.0   12845056.0      21.56%   13454336.0
1        features.1   64 224 224   64 224 224          0.0      12.25       3,211,264.0       3,211,264.0   12845056.0   12845056.0       0.92%   25690112.0
2        features.2   64 224 224   64 224 224      36928.0      12.25   3,699,376,128.0   1,852,899,328.0   12992768.0   12845056.0       4.74%   25837824.0
3        features.3   64 224 224   64 224 224          0.0      12.25       3,211,264.0       3,211,264.0   12845056.0   12845056.0       0.92%   25690112.0
4        features.4   64 224 224   64 112 112          0.0       3.06       2,408,448.0       3,211,264.0   12845056.0    3211264.0       1.22%   16056320.0
5        features.5   64 112 112  128 112 112      73856.0       6.12   1,849,688,064.0     926,449,664.0    3506688.0    6422528.0       4.71%    9929216.0
6        features.6  128 112 112  128 112 112          0.0       6.12       1,605,632.0       1,605,632.0    6422528.0    6422528.0       0.94%   12845056.0
7        features.7  128 112 112  128 112 112     147584.0       6.12   3,699,376,128.0   1,851,293,696.0    7012864.0    6422528.0       4.36%   13435392.0
8        features.8  128 112 112  128 112 112          0.0       6.12       1,605,632.0       1,605,632.0    6422528.0    6422528.0       0.91%   12845056.0
9        features.9  128 112 112  128  56  56          0.0       1.53       1,204,224.0       1,605,632.0    6422528.0    1605632.0       1.51%    8028160.0
10      features.10  128  56  56  256  56  56     295168.0       3.06   1,849,688,064.0     925,646,848.0    2786304.0    3211264.0       3.57%    5997568.0
11      features.11  256  56  56  256  56  56          0.0       3.06         802,816.0         802,816.0    3211264.0    3211264.0       0.90%    6422528.0
12      features.12  256  56  56  256  56  56     590080.0       3.06   3,699,376,128.0   1,850,490,880.0    5571584.0    3211264.0       4.30%    8782848.0
13      features.13  256  56  56  256  56  56          0.0       3.06         802,816.0         802,816.0    3211264.0    3211264.0       0.90%    6422528.0
14      features.14  256  56  56  256  56  56     590080.0       3.06   3,699,376,128.0   1,850,490,880.0    5571584.0    3211264.0       4.38%    8782848.0
15      features.15  256  56  56  256  56  56          0.0       3.06         802,816.0         802,816.0    3211264.0    3211264.0       0.94%    6422528.0
16      features.16  256  56  56  256  56  56     590080.0       3.06   3,699,376,128.0   1,850,490,880.0    5571584.0    3211264.0       4.33%    8782848.0
17      features.17  256  56  56  256  56  56          0.0       3.06         802,816.0         802,816.0    3211264.0    3211264.0       0.90%    6422528.0
18      features.18  256  56  56  256  28  28          0.0       0.77         602,112.0         802,816.0    3211264.0     802816.0       1.44%    4014080.0
19      features.19  256  28  28  512  28  28    1180160.0       1.53   1,849,688,064.0     925,245,440.0    5523456.0    1605632.0       3.60%    7129088.0
20      features.20  512  28  28  512  28  28          0.0       1.53         401,408.0         401,408.0    1605632.0    1605632.0       0.92%    3211264.0
21      features.21  512  28  28  512  28  28    2359808.0       1.53   3,699,376,128.0   1,850,089,472.0   11044864.0    1605632.0       4.45%   12650496.0
22      features.22  512  28  28  512  28  28          0.0       1.53         401,408.0         401,408.0    1605632.0    1605632.0       0.94%    3211264.0
23      features.23  512  28  28  512  28  28    2359808.0       1.53   3,699,376,128.0   1,850,089,472.0   11044864.0    1605632.0       4.39%   12650496.0
24      features.24  512  28  28  512  28  28          0.0       1.53         401,408.0         401,408.0    1605632.0    1605632.0       0.90%    3211264.0
25      features.25  512  28  28  512  28  28    2359808.0       1.53   3,699,376,128.0   1,850,089,472.0   11044864.0    1605632.0       4.34%   12650496.0
26      features.26  512  28  28  512  28  28          0.0       1.53         401,408.0         401,408.0    1605632.0    1605632.0       0.90%    3211264.0
27      features.27  512  28  28  512  14  14          0.0       0.38         301,056.0         401,408.0    1605632.0     401408.0       0.96%    2007040.0
28      features.28  512  14  14  512  14  14    2359808.0       0.38     924,844,032.0     462,522,368.0    9840640.0     401408.0       0.99%   10242048.0
29      features.29  512  14  14  512  14  14          0.0       0.38         100,352.0         100,352.0     401408.0     401408.0       0.00%     802816.0
30      features.30  512  14  14  512  14  14    2359808.0       0.38     924,844,032.0     462,522,368.0    9840640.0     401408.0       0.11%   10242048.0
31      features.31  512  14  14  512  14  14          0.0       0.38         100,352.0         100,352.0     401408.0     401408.0       0.00%     802816.0
32      features.32  512  14  14  512  14  14    2359808.0       0.38     924,844,032.0     462,522,368.0    9840640.0     401408.0       0.11%   10242048.0
33      features.33  512  14  14  512  14  14          0.0       0.38         100,352.0         100,352.0     401408.0     401408.0       0.00%     802816.0
34      features.34  512  14  14  512  14  14    2359808.0       0.38     924,844,032.0     462,522,368.0    9840640.0     401408.0       0.11%   10242048.0
35      features.35  512  14  14  512  14  14          0.0       0.38         100,352.0         100,352.0     401408.0     401408.0       0.00%     802816.0
36      features.36  512  14  14  512   7   7          0.0       0.10          75,264.0         100,352.0     401408.0     100352.0       0.01%     501760.0
37          avgpool  512   7   7  512   7   7          0.0       0.10               0.0               0.0          0.0          0.0       0.49%          0.0
38     classifier.0        25088         4096  102764544.0       0.02     205,516,800.0     102,760,448.0  411158528.0      16384.0      11.27%  411174912.0
39     classifier.1         4096         4096          0.0       0.02           4,096.0           4,096.0      16384.0      16384.0       0.00%      32768.0
40     classifier.2         4096         4096          0.0       0.02               0.0               0.0          0.0          0.0       0.01%          0.0
41     classifier.3         4096         4096   16781312.0       0.02      33,550,336.0      16,777,216.0   67141632.0      16384.0       1.08%   67158016.0
42     classifier.4         4096         4096          0.0       0.02           4,096.0           4,096.0      16384.0      16384.0       0.00%      32768.0
43     classifier.5         4096         4096          0.0       0.02               0.0               0.0          0.0          0.0       0.00%          0.0
44     classifier.6         4096         1000    4097000.0       0.00       8,191,000.0       4,096,000.0   16404384.0       4000.0       0.93%   16408384.0
total                                          143667240.0     119.34  39,283,567,128.0  19,667,896,320.0   16404384.0       4000.0     100.00%  825282624.0
============================================================================================================================================================
Total params: 143,667,240
------------------------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 119.34 MB
Total MAdd: 39.28 GMAdd
Total Flops: 19.67 GFlops
Total MemR+W: 787.05 MB

在为模型类定义对象后，只需打印模型

class RNN(nn.Module):
    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim):
        super().__init__()

        self.embedding = nn.Embedding(input_dim, embedding_dim)
        self.rnn = nn.RNN(embedding_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim, output_dim)
    def forward():
        ...

model = RNN(input_dim, embedding_dim, hidden_dim, output_dim)
print(model)

Keras模型总结使用torchsummary:

from torchsummary import summary
summary(model, input_size=(3, 224, 224))

torchinfo(以前的torchsummary)包产生类似Keras1的输出(对于给定的输入形状):2

from torchinfo import summary

model = ConvNet()
batch_size = 16
summary(model, input_size=(batch_size, 1, 28, 28))

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Conv2d (conv1): 1-1                    [5, 10, 24, 24]           260
├─Conv2d (conv2): 1-2                    [5, 20, 8, 8]             5,020
├─Dropout2d (conv2_drop): 1-3            [5, 20, 8, 8]             --
├─Linear (fc1): 1-4                      [5, 50]                   16,050
├─Linear (fc2): 1-5                      [5, 10]                   510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 7.69
==========================================================================================
Input size (MB): 0.05
Forward/backward pass size (MB): 0.91
Params size (MB): 0.09
Estimated Total Size (MB): 1.05
==========================================================================================

Notes: Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary()... Unlike Keras, PyTorch has a dynamic computational graph which can adapt to any compatible input shape across multiple calls e.g. any sufficiently large image size (for a fully convolutional network). As such, it cannot present an inherent set of input/output shapes for each layer, as these are input-dependent, and why in the above package you must specify the input dimensions.