torchsummaryX/README.md

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         0.144
1_conv2       [10, 20, 5, 5]    [1, 20, 8, 8]       5.02          0.32
2_conv2_drop               -    [1, 20, 8, 8]          -             -
3_fc1              [320, 50]          [1, 50]      16.05         0.016
4_fc2               [50, 10]          [1, 10]       0.51        0.0005
------------------------------------------------------------------------
Params (K):  21.84
Mult-Adds (M):  0.4805
========================================================================

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):  3784.5800000000004
Mult-Adds (M):  3.7763679999999997
==================================================================

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)))

===================================================================
           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]          -      28.901376
-------------------------------------------------------------------
Params (K):  36.928
Mult-Adds (M):  57.802752
===================================================================

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")