pytorch model summary, statistic parameters number, memory usage, MAdd and so on
use ResNet50 as an example
module name input shape output shape parameter quantity inference memory(MB) MAdd duration percent
0 conv1_Conv2d 3 224 224 64 112 112 9408 3.06MB 235,225,088 26.32%
1 bn1_BatchNorm2d 64 112 112 64 112 112 128 3.06MB 3,211,264 0.95%
2 relu_ReLU 64 112 112 64 112 112 0 3.06MB 802,816 0.61%
3 maxpool_MaxPool2d 64 112 112 64 56 56 0 0.77MB 1,605,632 1.64%
4 layer1.0.conv1_Conv2d 64 56 56 64 56 56 4096 0.77MB 25,489,408 0.34%
5 layer1.0.bn1_BatchNorm2d 64 56 56 64 56 56 128 0.77MB 802,816 0.16%
6 layer1.0.conv2_Conv2d 64 56 56 64 56 56 36864 0.77MB 231,010,304 2.47%
7 layer1.0.bn2_BatchNorm2d 64 56 56 64 56 56 128 0.77MB 802,816 0.23%
8 layer1.0.conv3_Conv2d 64 56 56 256 56 56 16384 3.06MB 101,957,632 0.68%
9 layer1.0.bn3_BatchNorm2d 256 56 56 256 56 56 512 3.06MB 3,211,264 0.94%
10 layer1.0.relu_ReLU 256 56 56 256 56 56 0 3.06MB 802,816 0.53%
11 layer1.0.downsample.0_Conv2d 64 56 56 256 56 56 16384 3.06MB 101,957,632 1.12%
12 layer1.0.downsample.1_BatchNorm2d 256 56 56 256 56 56 512 3.06MB 3,211,264 0.89%
13 layer1.1.conv1_Conv2d 256 56 56 64 56 56 16384 0.77MB 102,559,744 0.61%
14 layer1.1.bn1_BatchNorm2d 64 56 56 64 56 56 128 0.77MB 802,816 0.20%
15 layer1.1.conv2_Conv2d 64 56 56 64 56 56 36864 0.77MB 231,010,304 2.50%
16 layer1.1.bn2_BatchNorm2d 64 56 56 64 56 56 128 0.77MB 802,816 0.24%
17 layer1.1.conv3_Conv2d 64 56 56 256 56 56 16384 3.06MB 101,957,632 0.68%
18 layer1.1.bn3_BatchNorm2d 256 56 56 256 56 56 512 3.06MB 3,211,264 0.87%
19 layer1.1.relu_ReLU 256 56 56 256 56 56 0 3.06MB 802,816 0.47%
20 layer1.2.conv1_Conv2d 256 56 56 64 56 56 16384 0.77MB 102,559,744 0.87%
21 layer1.2.bn1_BatchNorm2d 64 56 56 64 56 56 128 0.77MB 802,816 0.19%
22 layer1.2.conv2_Conv2d 64 56 56 64 56 56 36864 0.77MB 231,010,304 2.50%
23 layer1.2.bn2_BatchNorm2d 64 56 56 64 56 56 128 0.77MB 802,816 0.17%
24 layer1.2.conv3_Conv2d 64 56 56 256 56 56 16384 3.06MB 101,957,632 0.65%
25 layer1.2.bn3_BatchNorm2d 256 56 56 256 56 56 512 3.06MB 3,211,264 0.82%
26 layer1.2.relu_ReLU 256 56 56 256 56 56 0 3.06MB 802,816 0.41%
27 layer2.0.conv1_Conv2d 256 56 56 128 56 56 32768 1.53MB 205,119,488 1.30%
28 layer2.0.bn1_BatchNorm2d 128 56 56 128 56 56 256 1.53MB 1,605,632 0.39%
29 layer2.0.conv2_Conv2d 128 56 56 128 28 28 147456 0.38MB 231,110,656 2.52%
30 layer2.0.bn2_BatchNorm2d 128 28 28 128 28 28 256 0.38MB 401,408 0.19%
31 layer2.0.conv3_Conv2d 128 28 28 512 28 28 65536 1.53MB 102,359,040 0.83%
32 layer2.0.bn3_BatchNorm2d 512 28 28 512 28 28 1024 1.53MB 1,605,632 0.70%
33 layer2.0.relu_ReLU 512 28 28 512 28 28 0 1.53MB 401,408 0.21%
34 layer2.0.downsample.0_Conv2d 256 56 56 512 28 28 131072 1.53MB 205,119,488 1.47%
35 layer2.0.downsample.1_BatchNorm2d 512 28 28 512 28 28 1024 1.53MB 1,605,632 0.68%
36 layer2.1.conv1_Conv2d 512 28 28 128 28 28 65536 0.38MB 102,660,096 0.33%
37 layer2.1.bn1_BatchNorm2d 128 28 28 128 28 28 256 0.38MB 401,408 0.12%
38 layer2.1.conv2_Conv2d 128 28 28 128 28 28 147456 0.38MB 231,110,656 1.98%
39 layer2.1.bn2_BatchNorm2d 128 28 28 128 28 28 256 0.38MB 401,408 0.19%
40 layer2.1.conv3_Conv2d 128 28 28 512 28 28 65536 1.53MB 102,359,040 0.50%
41 layer2.1.bn3_BatchNorm2d 512 28 28 512 28 28 1024 1.53MB 1,605,632 0.44%
42 layer2.1.relu_ReLU 512 28 28 512 28 28 0 1.53MB 401,408 0.14%
43 layer2.2.conv1_Conv2d 512 28 28 128 28 28 65536 0.38MB 102,660,096 0.70%
44 layer2.2.bn1_BatchNorm2d 128 28 28 128 28 28 256 0.38MB 401,408 0.18%
45 layer2.2.conv2_Conv2d 128 28 28 128 28 28 147456 0.38MB 231,110,656 1.43%
46 layer2.2.bn2_BatchNorm2d 128 28 28 128 28 28 256 0.38MB 401,408 0.18%
47 layer2.2.conv3_Conv2d 128 28 28 512 28 28 65536 1.53MB 102,359,040 0.62%
48 layer2.2.bn3_BatchNorm2d 512 28 28 512 28 28 1024 1.53MB 1,605,632 0.48%
49 layer2.2.relu_ReLU 512 28 28 512 28 28 0 1.53MB 401,408 0.15%
50 layer2.3.conv1_Conv2d 512 28 28 128 28 28 65536 0.38MB 102,660,096 0.47%
51 layer2.3.bn1_BatchNorm2d 128 28 28 128 28 28 256 0.38MB 401,408 0.17%
52 layer2.3.conv2_Conv2d 128 28 28 128 28 28 147456 0.38MB 231,110,656 1.20%
53 layer2.3.bn2_BatchNorm2d 128 28 28 128 28 28 256 0.38MB 401,408 0.12%
54 layer2.3.conv3_Conv2d 128 28 28 512 28 28 65536 1.53MB 102,359,040 0.79%
55 layer2.3.bn3_BatchNorm2d 512 28 28 512 28 28 1024 1.53MB 1,605,632 0.69%
56 layer2.3.relu_ReLU 512 28 28 512 28 28 0 1.53MB 401,408 0.18%
57 layer3.0.conv1_Conv2d 512 28 28 256 28 28 131072 0.77MB 205,320,192 0.69%
58 layer3.0.bn1_BatchNorm2d 256 28 28 256 28 28 512 0.77MB 802,816 0.23%
59 layer3.0.conv2_Conv2d 256 28 28 256 14 14 589824 0.19MB 231,160,832 1.12%
60 layer3.0.bn2_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
61 layer3.0.conv3_Conv2d 256 14 14 1024 14 14 262144 0.77MB 102,559,744 0.32%
62 layer3.0.bn3_BatchNorm2d 1024 14 14 1024 14 14 2048 0.77MB 802,816 0.63%
63 layer3.0.relu_ReLU 1024 14 14 1024 14 14 0 0.77MB 200,704 0.07%
64 layer3.0.downsample.0_Conv2d 512 28 28 1024 14 14 524288 0.77MB 205,320,192 1.41%
65 layer3.0.downsample.1_BatchNorm2d 1024 14 14 1024 14 14 2048 0.77MB 802,816 0.71%
66 layer3.1.conv1_Conv2d 1024 14 14 256 14 14 262144 0.19MB 102,710,272 0.29%
67 layer3.1.bn1_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
68 layer3.1.conv2_Conv2d 256 14 14 256 14 14 589824 0.19MB 231,160,832 0.90%
69 layer3.1.bn2_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
70 layer3.1.conv3_Conv2d 256 14 14 1024 14 14 262144 0.77MB 102,559,744 0.33%
71 layer3.1.bn3_BatchNorm2d 1024 14 14 1024 14 14 2048 0.77MB 802,816 0.44%
72 layer3.1.relu_ReLU 1024 14 14 1024 14 14 0 0.77MB 200,704 0.07%
73 layer3.2.conv1_Conv2d 1024 14 14 256 14 14 262144 0.19MB 102,710,272 0.62%
74 layer3.2.bn1_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.19%
75 layer3.2.conv2_Conv2d 256 14 14 256 14 14 589824 0.19MB 231,160,832 2.00%
76 layer3.2.bn2_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
77 layer3.2.conv3_Conv2d 256 14 14 1024 14 14 262144 0.77MB 102,559,744 0.33%
78 layer3.2.bn3_BatchNorm2d 1024 14 14 1024 14 14 2048 0.77MB 802,816 0.43%
79 layer3.2.relu_ReLU 1024 14 14 1024 14 14 0 0.77MB 200,704 0.07%
80 layer3.3.conv1_Conv2d 1024 14 14 256 14 14 262144 0.19MB 102,710,272 0.39%
81 layer3.3.bn1_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
82 layer3.3.conv2_Conv2d 256 14 14 256 14 14 589824 0.19MB 231,160,832 1.57%
83 layer3.3.bn2_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.16%
84 layer3.3.conv3_Conv2d 256 14 14 1024 14 14 262144 0.77MB 102,559,744 0.32%
85 layer3.3.bn3_BatchNorm2d 1024 14 14 1024 14 14 2048 0.77MB 802,816 0.42%
86 layer3.3.relu_ReLU 1024 14 14 1024 14 14 0 0.77MB 200,704 0.08%
87 layer3.4.conv1_Conv2d 1024 14 14 256 14 14 262144 0.19MB 102,710,272 0.41%
88 layer3.4.bn1_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
89 layer3.4.conv2_Conv2d 256 14 14 256 14 14 589824 0.19MB 231,160,832 1.09%
90 layer3.4.bn2_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.25%
91 layer3.4.conv3_Conv2d 256 14 14 1024 14 14 262144 0.77MB 102,559,744 0.66%
92 layer3.4.bn3_BatchNorm2d 1024 14 14 1024 14 14 2048 0.77MB 802,816 0.76%
93 layer3.4.relu_ReLU 1024 14 14 1024 14 14 0 0.77MB 200,704 0.10%
94 layer3.5.conv1_Conv2d 1024 14 14 256 14 14 262144 0.19MB 102,710,272 0.42%
95 layer3.5.bn1_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
96 layer3.5.conv2_Conv2d 256 14 14 256 14 14 589824 0.19MB 231,160,832 0.84%
97 layer3.5.bn2_BatchNorm2d 256 14 14 256 14 14 512 0.19MB 200,704 0.12%
98 layer3.5.conv3_Conv2d 256 14 14 1024 14 14 262144 0.77MB 102,559,744 0.44%
99 layer3.5.bn3_BatchNorm2d 1024 14 14 1024 14 14 2048 0.77MB 802,816 0.55%
100 layer3.5.relu_ReLU 1024 14 14 1024 14 14 0 0.77MB 200,704 0.15%
101 layer4.0.conv1_Conv2d 1024 14 14 512 14 14 524288 0.38MB 205,420,544 1.00%
102 layer4.0.bn1_BatchNorm2d 512 14 14 512 14 14 1024 0.38MB 401,408 0.44%
103 layer4.0.conv2_Conv2d 512 14 14 512 7 7 2359296 0.10MB 231,185,920 1.63%
104 layer4.0.bn2_BatchNorm2d 512 7 7 512 7 7 1024 0.10MB 100,352 0.16%
105 layer4.0.conv3_Conv2d 512 7 7 2048 7 7 1048576 0.38MB 102,660,096 0.31%
106 layer4.0.bn3_BatchNorm2d 2048 7 7 2048 7 7 4096 0.38MB 401,408 0.62%
107 layer4.0.relu_ReLU 2048 7 7 2048 7 7 0 0.38MB 100,352 0.04%
108 layer4.0.downsample.0_Conv2d 1024 14 14 2048 7 7 2097152 0.38MB 205,420,544 0.61%
109 layer4.0.downsample.1_BatchNorm2d 2048 7 7 2048 7 7 4096 0.38MB 401,408 0.62%
110 layer4.1.conv1_Conv2d 2048 7 7 512 7 7 1048576 0.10MB 102,735,360 0.35%
111 layer4.1.bn1_BatchNorm2d 512 7 7 512 7 7 1024 0.10MB 100,352 0.16%
112 layer4.1.conv2_Conv2d 512 7 7 512 7 7 2359296 0.10MB 231,185,920 0.78%
113 layer4.1.bn2_BatchNorm2d 512 7 7 512 7 7 1024 0.10MB 100,352 0.16%
114 layer4.1.conv3_Conv2d 512 7 7 2048 7 7 1048576 0.38MB 102,660,096 0.94%
115 layer4.1.bn3_BatchNorm2d 2048 7 7 2048 7 7 4096 0.38MB 401,408 1.17%
116 layer4.1.relu_ReLU 2048 7 7 2048 7 7 0 0.38MB 100,352 0.04%
117 layer4.2.conv1_Conv2d 2048 7 7 512 7 7 1048576 0.10MB 102,735,360 0.33%
118 layer4.2.bn1_BatchNorm2d 512 7 7 512 7 7 1024 0.10MB 100,352 0.16%
119 layer4.2.conv2_Conv2d 512 7 7 512 7 7 2359296 0.10MB 231,185,920 0.78%
120 layer4.2.bn2_BatchNorm2d 512 7 7 512 7 7 1024 0.10MB 100,352 0.17%
121 layer4.2.conv3_Conv2d 512 7 7 2048 7 7 1048576 0.38MB 102,660,096 0.29%
122 layer4.2.bn3_BatchNorm2d 2048 7 7 2048 7 7 4096 0.38MB 401,408 0.63%
123 layer4.2.relu_ReLU 2048 7 7 2048 7 7 0 0.38MB 100,352 0.05%
124 avgpool_AvgPool2d 2048 7 7 2048 1 1 0 0.01MB 100,352 0.05%
125 fc_Linear 2048 1000 2049000 0.00MB 4,095,000 0.66%
=========================================================================================================================================
total parameters quantity: 25,557,032
total memory: 109.69MB
total MAdd: 8,219,737,624