PyTorch model summary
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