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https://github.com/wassname/torchsummaryX.git
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Add example of large resnet model
I thought perhaps we could add an example of the outputs on a large model. I noticed that some of these summary packages break down in the presence of large models, even though these are the ones you most need to summarise.
This commit is contained in:
Mike Clark
@@ -124,3 +124,152 @@ class Net(nn.Module):
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return out
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summary(Net(), torch.zeros((1, 64, 28, 28)), "args1", args2="args2")
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```
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```
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===================================================================
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Kernel Shape Output Shape Params (K) Mult-Adds (M)
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Layer
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0_conv1 [64, 64, 3, 3] [1, 64, 28, 28] 36.928 28.901376
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1_conv1 [64, 64, 3, 3] [1, 64, 28, 28] - 28.901376
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-------------------------------------------------------------------
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Params (K): 36.928
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Mult-Adds (M): 57.802752
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===================================================================
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```
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Large models with long layer names
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```python
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import torchvision
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model = torchvision.models.resnet18()
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summary(model, torch.zeros(4, 3, 224, 224))
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```
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```
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Layer
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0_conv1 [3, 64, 7, 7] [4, 64, 112, 112]
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1_bn1 [64] [4, 64, 112, 112]
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2_relu - [4, 64, 112, 112]
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3_maxpool - [4, 64, 56, 56]
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4_layer1.0.Conv2d_conv1 [64, 64, 3, 3] [4, 64, 56, 56]
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5_layer1.0.BatchNorm2d_bn1 [64] [4, 64, 56, 56]
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6_layer1.0.ReLU_relu - [4, 64, 56, 56]
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7_layer1.0.Conv2d_conv2 [64, 64, 3, 3] [4, 64, 56, 56]
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8_layer1.0.BatchNorm2d_bn2 [64] [4, 64, 56, 56]
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9_layer1.0.ReLU_relu - [4, 64, 56, 56]
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10_layer1.1.Conv2d_conv1 [64, 64, 3, 3] [4, 64, 56, 56]
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11_layer1.1.BatchNorm2d_bn1 [64] [4, 64, 56, 56]
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12_layer1.1.ReLU_relu - [4, 64, 56, 56]
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13_layer1.1.Conv2d_conv2 [64, 64, 3, 3] [4, 64, 56, 56]
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14_layer1.1.BatchNorm2d_bn2 [64] [4, 64, 56, 56]
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15_layer1.1.ReLU_relu - [4, 64, 56, 56]
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16_layer2.0.Conv2d_conv1 [64, 128, 3, 3] [4, 128, 28, 28]
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17_layer2.0.BatchNorm2d_bn1 [128] [4, 128, 28, 28]
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18_layer2.0.ReLU_relu - [4, 128, 28, 28]
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19_layer2.0.Conv2d_conv2 [128, 128, 3, 3] [4, 128, 28, 28]
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20_layer2.0.BatchNorm2d_bn2 [128] [4, 128, 28, 28]
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21_layer2.0.downsample.Conv2d_0 [64, 128, 1, 1] [4, 128, 28, 28]
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22_layer2.0.downsample.BatchNorm2d_1 [128] [4, 128, 28, 28]
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23_layer2.0.ReLU_relu - [4, 128, 28, 28]
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24_layer2.1.Conv2d_conv1 [128, 128, 3, 3] [4, 128, 28, 28]
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25_layer2.1.BatchNorm2d_bn1 [128] [4, 128, 28, 28]
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26_layer2.1.ReLU_relu - [4, 128, 28, 28]
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27_layer2.1.Conv2d_conv2 [128, 128, 3, 3] [4, 128, 28, 28]
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28_layer2.1.BatchNorm2d_bn2 [128] [4, 128, 28, 28]
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29_layer2.1.ReLU_relu - [4, 128, 28, 28]
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30_layer3.0.Conv2d_conv1 [128, 256, 3, 3] [4, 256, 14, 14]
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31_layer3.0.BatchNorm2d_bn1 [256] [4, 256, 14, 14]
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32_layer3.0.ReLU_relu - [4, 256, 14, 14]
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33_layer3.0.Conv2d_conv2 [256, 256, 3, 3] [4, 256, 14, 14]
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34_layer3.0.BatchNorm2d_bn2 [256] [4, 256, 14, 14]
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35_layer3.0.downsample.Conv2d_0 [128, 256, 1, 1] [4, 256, 14, 14]
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36_layer3.0.downsample.BatchNorm2d_1 [256] [4, 256, 14, 14]
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37_layer3.0.ReLU_relu - [4, 256, 14, 14]
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38_layer3.1.Conv2d_conv1 [256, 256, 3, 3] [4, 256, 14, 14]
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39_layer3.1.BatchNorm2d_bn1 [256] [4, 256, 14, 14]
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40_layer3.1.ReLU_relu - [4, 256, 14, 14]
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41_layer3.1.Conv2d_conv2 [256, 256, 3, 3] [4, 256, 14, 14]
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42_layer3.1.BatchNorm2d_bn2 [256] [4, 256, 14, 14]
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43_layer3.1.ReLU_relu - [4, 256, 14, 14]
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44_layer4.0.Conv2d_conv1 [256, 512, 3, 3] [4, 512, 7, 7]
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45_layer4.0.BatchNorm2d_bn1 [512] [4, 512, 7, 7]
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46_layer4.0.ReLU_relu - [4, 512, 7, 7]
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47_layer4.0.Conv2d_conv2 [512, 512, 3, 3] [4, 512, 7, 7]
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48_layer4.0.BatchNorm2d_bn2 [512] [4, 512, 7, 7]
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49_layer4.0.downsample.Conv2d_0 [256, 512, 1, 1] [4, 512, 7, 7]
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50_layer4.0.downsample.BatchNorm2d_1 [512] [4, 512, 7, 7]
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51_layer4.0.ReLU_relu - [4, 512, 7, 7]
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52_layer4.1.Conv2d_conv1 [512, 512, 3, 3] [4, 512, 7, 7]
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53_layer4.1.BatchNorm2d_bn1 [512] [4, 512, 7, 7]
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54_layer4.1.ReLU_relu - [4, 512, 7, 7]
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55_layer4.1.Conv2d_conv2 [512, 512, 3, 3] [4, 512, 7, 7]
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56_layer4.1.BatchNorm2d_bn2 [512] [4, 512, 7, 7]
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57_layer4.1.ReLU_relu - [4, 512, 7, 7]
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58_avgpool - [4, 512, 1, 1]
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59_fc [512, 1000] [4, 1000]
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Params (K) Mult-Adds (M)
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Layer
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0_conv1 9.408 118.014
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1_bn1 0.128 6.4e-05
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2_relu - -
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3_maxpool - -
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4_layer1.0.Conv2d_conv1 36.864 115.606
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5_layer1.0.BatchNorm2d_bn1 0.128 6.4e-05
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6_layer1.0.ReLU_relu - -
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7_layer1.0.Conv2d_conv2 36.864 115.606
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8_layer1.0.BatchNorm2d_bn2 0.128 6.4e-05
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9_layer1.0.ReLU_relu - -
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10_layer1.1.Conv2d_conv1 36.864 115.606
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11_layer1.1.BatchNorm2d_bn1 0.128 6.4e-05
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12_layer1.1.ReLU_relu - -
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13_layer1.1.Conv2d_conv2 36.864 115.606
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14_layer1.1.BatchNorm2d_bn2 0.128 6.4e-05
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15_layer1.1.ReLU_relu - -
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16_layer2.0.Conv2d_conv1 73.728 57.8028
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17_layer2.0.BatchNorm2d_bn1 0.256 0.000128
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18_layer2.0.ReLU_relu - -
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19_layer2.0.Conv2d_conv2 147.456 115.606
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20_layer2.0.BatchNorm2d_bn2 0.256 0.000128
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21_layer2.0.downsample.Conv2d_0 8.192 6.42253
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22_layer2.0.downsample.BatchNorm2d_1 0.256 0.000128
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23_layer2.0.ReLU_relu - -
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24_layer2.1.Conv2d_conv1 147.456 115.606
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25_layer2.1.BatchNorm2d_bn1 0.256 0.000128
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26_layer2.1.ReLU_relu - -
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27_layer2.1.Conv2d_conv2 147.456 115.606
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28_layer2.1.BatchNorm2d_bn2 0.256 0.000128
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29_layer2.1.ReLU_relu - -
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30_layer3.0.Conv2d_conv1 294.912 57.8028
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31_layer3.0.BatchNorm2d_bn1 0.512 0.000256
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32_layer3.0.ReLU_relu - -
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33_layer3.0.Conv2d_conv2 589.824 115.606
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34_layer3.0.BatchNorm2d_bn2 0.512 0.000256
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35_layer3.0.downsample.Conv2d_0 32.768 6.42253
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36_layer3.0.downsample.BatchNorm2d_1 0.512 0.000256
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37_layer3.0.ReLU_relu - -
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38_layer3.1.Conv2d_conv1 589.824 115.606
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39_layer3.1.BatchNorm2d_bn1 0.512 0.000256
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40_layer3.1.ReLU_relu - -
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41_layer3.1.Conv2d_conv2 589.824 115.606
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42_layer3.1.BatchNorm2d_bn2 0.512 0.000256
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43_layer3.1.ReLU_relu - -
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44_layer4.0.Conv2d_conv1 1179.65 57.8028
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45_layer4.0.BatchNorm2d_bn1 1.024 0.000512
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46_layer4.0.ReLU_relu - -
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47_layer4.0.Conv2d_conv2 2359.3 115.606
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48_layer4.0.BatchNorm2d_bn2 1.024 0.000512
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49_layer4.0.downsample.Conv2d_0 131.072 6.42253
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50_layer4.0.downsample.BatchNorm2d_1 1.024 0.000512
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51_layer4.0.ReLU_relu - -
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52_layer4.1.Conv2d_conv1 2359.3 115.606
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53_layer4.1.BatchNorm2d_bn1 1.024 0.000512
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54_layer4.1.ReLU_relu - -
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55_layer4.1.Conv2d_conv2 2359.3 115.606
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56_layer4.1.BatchNorm2d_bn2 1.024 0.000512
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57_layer4.1.ReLU_relu - -
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58_avgpool - -
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59_fc 513 0.512
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----------------------------------------------------------------------------------------------------
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Params (K): 11689.511999999999
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Mult-Adds (M): 1814.0781440000007
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====================================================================================================
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```
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