mirror of
https://github.com/wassname/torchsummaryX.git
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138 lines
5.3 KiB
Markdown
138 lines
5.3 KiB
Markdown
# torchsummaryX
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Improved visualization tool of [torchsummary](https://github.com/sksq96/pytorch-summary). Here, it visualizes kernel size, output shape, # params, and Mult-Adds. Also the torchsummaryX can handle RNN, Recursive NN, or model with multiple inputs.
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## Usage
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`pip install torchsummaryX` and
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```python
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from torchsummaryX import summary
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summary(your_model, torch.zeros((1, 3, 224, 224)))
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```
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Args:
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- `model` (Module): Model to summarize
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- `x` (Tensor): Input tensor of the model with [N, C, H, W] shape dtype and device have to match to the model
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- `args, kwargs`: Other arguments used in `model.forward` function
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## Examples
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CNN for MNIST
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```python
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
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self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
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self.conv2_drop = nn.Dropout2d()
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self.fc1 = nn.Linear(320, 50)
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self.fc2 = nn.Linear(50, 10)
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def forward(self, x):
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x = F.relu(F.max_pool2d(self.conv1(x), 2))
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x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
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x = x.view(-1, 320)
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x = F.relu(self.fc1(x))
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x = F.dropout(x, training=self.training)
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x = self.fc2(x)
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return F.log_softmax(x, dim=1)
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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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Layer
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0_conv1 [1, 10, 5, 5] [1, 10, 24, 24] 0.26 0.1440
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1_conv2 [10, 20, 5, 5] [1, 20, 8, 8] 5.02 0.3200
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2_conv2_drop - [1, 20, 8, 8] NaN NaN
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3_fc1 [320, 50] [1, 50] 16.05 0.0160
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4_fc2 [50, 10] [1, 10] 0.51 0.0005
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====================================================================================================
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Params (K) 21.8400
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Mult-Adds (M) 0.4805
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dtype: float64
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====================================================================================================
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```
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RNN
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```python
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class Net(nn.Module):
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def __init__(self,
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vocab_size=20, embed_dim=300,
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hidden_dim=512, num_layers=2):
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super().__init__()
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self.hidden_dim = hidden_dim
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self.embedding = nn.Embedding(vocab_size, embed_dim)
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self.encoder = nn.LSTM(embed_dim, hidden_dim,
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num_layers=num_layers)
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self.decoder = nn.Linear(hidden_dim, vocab_size)
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def forward(self, x):
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embed = self.embedding(x)
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out, hidden = self.encoder(embed)
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out = self.decoder(out)
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out = out.view(-1, out.size(2))
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return out, hidden
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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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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) 3784.580000
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Mult-Adds (M) 3.776368
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dtype: float64
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====================================================================================================
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```
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Recursive NN
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```python
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)
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def forward(self, x):
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out = self.conv1(x)
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out = self.conv1(out)
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return out
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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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Layer Kernel Shape Output Shape # Params (K) # Mult-Adds (M)
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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] NaN 28.901376
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====================================================================================================
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Kernel Shape [64, 64, 3, 3, 64, 64, 3, 3]
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Output Shape [1, 64, 28, 28, 1, 64, 28, 28]
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Params (K) 36.928
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Mult-Adds (M) 57.8028
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dtype: object
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====================================================================================================
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# Params: 36.93K
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# Mult-Adds: 57.80M
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----------------------------------------------------------------------------------------------------
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```
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Multiple arguments
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```python
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)
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def forward(self, x, args1, args2):
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out = self.conv1(x)
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out = self.conv1(out)
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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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