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Update README.md
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Namhyuk Ahn
@@ -36,18 +36,21 @@ class Net(nn.Module):
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summary(Net(), torch.zeros((1, 1, 28, 28)))
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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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=================================================================
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Kernel Shape Output Shape Params Mult-Adds
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Layer
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0_conv1 [1, 10, 5, 5] [1, 10, 24, 24] 0.26 0.144
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1_conv2 [10, 20, 5, 5] [1, 20, 8, 8] 5.02 0.32
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2_conv2_drop - [1, 20, 8, 8] - -
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3_fc1 [320, 50] [1, 50] 16.05 0.016
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4_fc2 [50, 10] [1, 10] 0.51 0.0005
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------------------------------------------------------------------------
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Params (K): 21.84
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Mult-Adds (M): 0.4805
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========================================================================
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0_conv1 [1, 10, 5, 5] [1, 10, 24, 24] 260.0 144.0k
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1_conv2 [10, 20, 5, 5] [1, 20, 8, 8] 5.02k 320.0k
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2_conv2_drop - [1, 20, 8, 8] - -
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3_fc1 [320, 50] [1, 50] 16.05k 16.0k
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4_fc2 [50, 10] [1, 10] 510.0 500.0
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-----------------------------------------------------------------
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Totals
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Total params 21.84k
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Trainable params 21.84k
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Non-trainable params 0.0
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Mult-Adds 480.5k
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=================================================================
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```
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RNN
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@@ -74,16 +77,19 @@ inputs = torch.zeros((100, 1), dtype=torch.long) # [length, batch_size]
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summary(Net(), inputs)
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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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===========================================================
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Kernel Shape Output Shape Params Mult-Adds
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Layer
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0_embedding [300, 20] [100, 1, 300] 6.00 0.006000
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1_encoder - [100, 1, 512] 3768.32 3.760128
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2_decoder [512, 20] [100, 1, 20] 10.26 0.010240
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------------------------------------------------------------------
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Params (K): 3784.5800000000004
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Mult-Adds (M): 3.7763679999999997
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==================================================================
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0_embedding [300, 20] [100, 1, 300] 6000 6000
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1_encoder - [100, 1, 512] 3768320 3760128
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2_decoder [512, 20] [100, 1, 20] 10260 10240
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-----------------------------------------------------------
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Totals
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Total params 3784580
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Trainable params 3784580
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Non-trainable params 0
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Mult-Adds 3776368
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===========================================================
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```
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Recursive NN
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@@ -100,15 +106,18 @@ class Net(nn.Module):
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summary(Net(), torch.zeros((1, 64, 28, 28)))
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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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============================================================
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Kernel Shape Output Shape Params Mult-Adds
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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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0_conv1 [64, 64, 3, 3] [1, 64, 28, 28] 36.928k 28901376
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1_conv1 [64, 64, 3, 3] [1, 64, 28, 28] - 28901376
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------------------------------------------------------------
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Totals
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Total params 36.928k
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Trainable params 36.928k
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Non-trainable params 0.0
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Mult-Adds 57.802752M
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============================================================
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```
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Multiple arguments
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@@ -125,18 +134,6 @@ class Net(nn.Module):
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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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@@ -144,6 +141,8 @@ 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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=================================================================================================
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Kernel Shape Output Shape \
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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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@@ -206,70 +205,73 @@ Layer
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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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Params Mult-Adds
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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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0_conv1 9.408k 118.013952M
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1_bn1 128.0 64.0
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2_relu - -
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3_maxpool - -
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4_layer1.0.Conv2d_conv1 36.864k 115.605504M
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5_layer1.0.BatchNorm2d_bn1 128.0 64.0
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6_layer1.0.ReLU_relu - -
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7_layer1.0.Conv2d_conv2 36.864k 115.605504M
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8_layer1.0.BatchNorm2d_bn2 128.0 64.0
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9_layer1.0.ReLU_relu - -
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10_layer1.1.Conv2d_conv1 36.864k 115.605504M
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11_layer1.1.BatchNorm2d_bn1 128.0 64.0
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12_layer1.1.ReLU_relu - -
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13_layer1.1.Conv2d_conv2 36.864k 115.605504M
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14_layer1.1.BatchNorm2d_bn2 128.0 64.0
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15_layer1.1.ReLU_relu - -
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16_layer2.0.Conv2d_conv1 73.728k 57.802752M
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17_layer2.0.BatchNorm2d_bn1 256.0 128.0
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18_layer2.0.ReLU_relu - -
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19_layer2.0.Conv2d_conv2 147.456k 115.605504M
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20_layer2.0.BatchNorm2d_bn2 256.0 128.0
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21_layer2.0.downsample.Conv2d_0 8.192k 6.422528M
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22_layer2.0.downsample.BatchNorm2d_1 256.0 128.0
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23_layer2.0.ReLU_relu - -
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24_layer2.1.Conv2d_conv1 147.456k 115.605504M
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25_layer2.1.BatchNorm2d_bn1 256.0 128.0
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26_layer2.1.ReLU_relu - -
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27_layer2.1.Conv2d_conv2 147.456k 115.605504M
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28_layer2.1.BatchNorm2d_bn2 256.0 128.0
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29_layer2.1.ReLU_relu - -
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30_layer3.0.Conv2d_conv1 294.912k 57.802752M
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31_layer3.0.BatchNorm2d_bn1 512.0 256.0
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32_layer3.0.ReLU_relu - -
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33_layer3.0.Conv2d_conv2 589.824k 115.605504M
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34_layer3.0.BatchNorm2d_bn2 512.0 256.0
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35_layer3.0.downsample.Conv2d_0 32.768k 6.422528M
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36_layer3.0.downsample.BatchNorm2d_1 512.0 256.0
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37_layer3.0.ReLU_relu - -
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38_layer3.1.Conv2d_conv1 589.824k 115.605504M
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39_layer3.1.BatchNorm2d_bn1 512.0 256.0
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40_layer3.1.ReLU_relu - -
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41_layer3.1.Conv2d_conv2 589.824k 115.605504M
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42_layer3.1.BatchNorm2d_bn2 512.0 256.0
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43_layer3.1.ReLU_relu - -
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44_layer4.0.Conv2d_conv1 1.179648M 57.802752M
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45_layer4.0.BatchNorm2d_bn1 1.024k 512.0
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46_layer4.0.ReLU_relu - -
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47_layer4.0.Conv2d_conv2 2.359296M 115.605504M
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48_layer4.0.BatchNorm2d_bn2 1.024k 512.0
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49_layer4.0.downsample.Conv2d_0 131.072k 6.422528M
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50_layer4.0.downsample.BatchNorm2d_1 1.024k 512.0
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51_layer4.0.ReLU_relu - -
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52_layer4.1.Conv2d_conv1 2.359296M 115.605504M
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53_layer4.1.BatchNorm2d_bn1 1.024k 512.0
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54_layer4.1.ReLU_relu - -
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55_layer4.1.Conv2d_conv2 2.359296M 115.605504M
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56_layer4.1.BatchNorm2d_bn2 1.024k 512.0
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57_layer4.1.ReLU_relu - -
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58_avgpool - -
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59_fc 513.0k 512.0k
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-------------------------------------------------------------------------------------------------
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Totals
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Total params 11.689512M
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Trainable params 11.689512M
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Non-trainable params 0.0
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Mult-Adds 1.814078144G
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=================================================================================================
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```
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