torchsummaryX

Improved visualization tool of torchsummary. Here, it visualizes kernel size, output shape, # params, and Mult-Adds. Also the torchsummaryX can handle RNN, Recursive NN, or model with multiple inputs.

Usage

pip install torchsummaryX and

from torchsummaryX import summary
summary(your_model, torch.zeros((1, 3, 224, 224)))

Args:

• model (Module): Model to summarize
• x (Tensor): Input tensor of the model with [N, C, H, W] shape dtype and device have to match to the model
• args, kwargs: Other arguments used in model.forward function

Examples

CNN for MNIST

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)
summary(Net(), torch.zeros((1, 1, 28, 28)))

====================================================================================================
                Kernel Shape     Output Shape  Params (K)  Mult-Adds (M)
Layer                                                                   
0_conv1        [1, 10, 5, 5]  [1, 10, 24, 24]        0.26         0.1440
1_conv2       [10, 20, 5, 5]    [1, 20, 8, 8]        5.02         0.3200
2_conv2_drop               -    [1, 20, 8, 8]         NaN            NaN
3_fc1              [320, 50]          [1, 50]       16.05         0.0160
4_fc2               [50, 10]          [1, 10]        0.51         0.0005
====================================================================================================
Params (K)       21.8400
Mult-Adds (M)     0.4805
dtype: float64
====================================================================================================

RNN

class Net(nn.Module):
    def __init__(self,
                 vocab_size=20, embed_dim=300,
                 hidden_dim=512, num_layers=2):
        super().__init__()

        self.hidden_dim = hidden_dim
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.encoder = nn.LSTM(embed_dim, hidden_dim,
                               num_layers=num_layers)
        self.decoder = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x):
        embed = self.embedding(x)
        out, hidden = self.encoder(embed)
        out = self.decoder(out)
        out = out.view(-1, out.size(2))
        return out, hidden
inputs = torch.zeros((100, 1), dtype=torch.long) # [length, batch_size]
summary(Net(), inputs)

====================================================================================================
            Kernel Shape   Output Shape  Params (K)  Mult-Adds (M)
Layer                                                             
0_embedding    [300, 20]  [100, 1, 300]        6.00       0.006000
1_encoder              -  [100, 1, 512]     3768.32       3.760128
2_decoder      [512, 20]   [100, 1, 20]       10.26       0.010240
====================================================================================================
Params (K)       3784.580000
Mult-Adds (M)       3.776368
dtype: float64
====================================================================================================

Recursive NN

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv1(out)
        return out
summary(Net(), torch.zeros((1, 64, 28, 28)))

----------------------------------------------------------------------------------------------------
Layer                   Kernel Shape         Output Shape         # Params (K)      # Mult-Adds (M)
====================================================================================================
           Kernel Shape     Output Shape  Params (K)  Mult-Adds (M)
Layer                                                              
0_conv1  [64, 64, 3, 3]  [1, 64, 28, 28]      36.928      28.901376
1_conv1  [64, 64, 3, 3]  [1, 64, 28, 28]         NaN      28.901376
====================================================================================================
Kernel Shape       [64, 64, 3, 3, 64, 64, 3, 3]
Output Shape     [1, 64, 28, 28, 1, 64, 28, 28]
Params (K)                               36.928
Mult-Adds (M)                           57.8028
dtype: object
====================================================================================================
# Params:    36.93K
# Mult-Adds: 57.80M
----------------------------------------------------------------------------------------------------

Multiple arguments

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)

    def forward(self, x, args1, args2):
        out = self.conv1(x)
        out = self.conv1(out)
        return out
summary(Net(), torch.zeros((1, 64, 28, 28)), "args1", args2="args2")