torch-summary 1.3.0

Model summary in PyTorch, based off of the original torchsummary.

torch-summary

Torch-summary provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary() API to view the visualization of the model, which is helpful while debugging your network. In this project, we implement a similar functionality in PyTorch and create a clean, simple interface to use in your projects.

This is a completely rewritten version of the original torchsummary and torchsummaryX projects by @sksq96 and @nmhkahn. There are quite a few pull requests on the original project (which hasn't been updated in over a year), so I decided to improve and consolidate all of the old features and the new feature requests.

This version now supports:

• RNNs, LSTMs, and other recursive layers
• Branching output to explore model layers using specified depths
• Returns ModelStatistics object to access summary data
• Configurable columns of returned data

Other new features:

• Verbose mode to show specific weights and bias layers
• Accepts either input data or simply the input shape to work!
• Customizable widths and custom batch dimension.
• More comprehensive testing using pytest

Usage

pip install torch-summary

or

git clone https://github.com/tyleryep/torch-summary.git

from torchsummary import summary

summary(your_model, input_data)

Documentation

"""
Summarize the given PyTorch model. Summarized information includes:
    1) output shape,
    2) kernel shape,
    3) number of the parameters
    4) operations (Mult-Adds)

Arguments:
    model (nn.Module): PyTorch model to summarize
    input_data (Sequence of Sizes or Tensors):
        Example input tensor of the model (dtypes inferred from model input).
        - OR -
        Shape of input data as a List/Tuple/torch.Size (dtypes must match model input,
        default is FloatTensors).
    batch_dim (int): batch_dimension of input data
    branching (bool): Whether to use the branching layout for the printed output.
    col_names (Sequence[str]): specify which columns to show in the output.
        Currently supported:
        ('output_size', 'num_params', 'kernel_size', 'mult_adds')
    col_width (int): width of each column
    depth (int): number of nested layers to traverse (e.g. Sequentials)
    device (torch.Device): Uses this torch device for model and input_data.
        Defaults to torch.cuda.is_available().
    dtypes (List[torch.dtype]): for multiple inputs, specify the size of both inputs, and
        also specify the types of each parameter here.
    verbose (int):
        0 (quiet): No output
        1 (default): Print model summary
        2 (verbose): Show weight and bias layers in full detail
    args, kwargs: Other arguments used in `model.forward` function.
"""

Examples

Get Model Summary as String

from torchsummary import summary

model_stats = summary(your_model, input_data=(C, H, W), verbose=0)
summary_str = str(model_stats)

ConvNets

class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d(0.3)
        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 = self.fc2(x)
        return F.log_softmax(x, dim=1)


model = CNN()
summary(model, (1, 28, 28))

------------------------------------------------------------------------------------------
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Conv2d: 1-1                            [-1, 10, 24, 24]          260
├─Conv2d: 1-2                            [-1, 20, 8, 8]            5,020
├─Dropout2d: 1-3                         [-1, 20, 8, 8]            --
├─Linear: 1-4                            [-1, 50]                  16,050
├─Linear: 1-5                            [-1, 10]                  510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
------------------------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.05
Params size (MB): 0.08
Estimated Total Size (MB): 0.14
------------------------------------------------------------------------------------------

Multiple Inputs w/ Different Data Types

class MultipleInputNetDifferentDtypes(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1a = nn.Linear(300, 50)
        self.fc1b = nn.Linear(50, 10)

        self.fc2a = nn.Linear(300, 50)
        self.fc2b = nn.Linear(50, 10)

    def forward(self, x1, x2):
        x1 = F.relu(self.fc1a(x1))
        x1 = self.fc1b(x1)
        x2 = x2.type(torch.float)
        x2 = F.relu(self.fc2a(x2))
        x2 = self.fc2b(x2)
        x = torch.cat((x1, x2), 0)
        return F.log_softmax(x, dim=1)


summary(model, [(1, 300), (1, 300)], dtypes=[torch.float, torch.long])

Alternatively, you can also pass in the input_data itself, and torchsummary will automatically infer the data types.

input_data = torch.randn(1, 300)
other_input_data = torch.randn(1, 300).long()
model = MultipleInputNetDifferentDtypes()

summary(model, input_data, other_input_data, ...)

Explore Different Configurations

class LSTMNet(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

summary(
    LSTMNet(),
    (100,),
    dtypes=[torch.long],
    branching=False,
    verbose=2,
    col_width=16,
    col_names=["kernel_size", "output_size", "num_params", "mult_adds"],
)

--------------------------------------------------------------------------------------------------------
Layer (type:depth-idx)         Kernel Shape         Output Shape         Param #          Mult-Adds
========================================================================================================
Embedding: 1-1                 [300, 20]            [-1, 100, 300]       6,000            6,000
LSTM: 1-2                       --                  [2, 100, 512]        3,768,320        3,760,128
  weight_ih_l0                 [2048, 300]
  weight_hh_l0                 [2048, 512]
  weight_ih_l1                 [2048, 512]
  weight_hh_l1                 [2048, 512]
Linear: 1-3                    [512, 20]            [-1, 100, 20]        10,260           10,240
========================================================================================================
Total params: 3,784,580
Trainable params: 3,784,580
Non-trainable params: 0
--------------------------------------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 1.03
Params size (MB): 14.44
Estimated Total Size (MB): 15.46
--------------------------------------------------------------------------------------------------------

ResNet

import torchvision

model = torchvision.models.resnet50()
summary(model, (3, 224, 224), depth=3)

------------------------------------------------------------------------------------------
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Conv2d: 1-1                            [-1, 64, 112, 112]        9,408
├─BatchNorm2d: 1-2                       [-1, 64, 112, 112]        128
├─ReLU: 1-3                              [-1, 64, 112, 112]        --
├─MaxPool2d: 1-4                         [-1, 64, 56, 56]          --
├─Sequential: 1-5                        [-1, 256, 56, 56]         --
|    └─Bottleneck: 2-1                   [-1, 256, 56, 56]         --
|    |    └─Conv2d: 3-1                  [-1, 64, 56, 56]          4,096
|    |    └─BatchNorm2d: 3-2             [-1, 64, 56, 56]          128
|    |    └─ReLU: 3-3                    [-1, 64, 56, 56]          --
|    |    └─Conv2d: 3-4                  [-1, 64, 56, 56]          36,864
|    |    └─BatchNorm2d: 3-5             [-1, 64, 56, 56]          128
|    |    └─ReLU: 3-6                    [-1, 64, 56, 56]          --
|    |    └─Conv2d: 3-7                  [-1, 256, 56, 56]         16,384
|    |    └─BatchNorm2d: 3-8             [-1, 256, 56, 56]         512
|    |    └─Sequential: 3-9              [-1, 256, 56, 56]         --
|    |    └─ReLU: 3-10                   [-1, 256, 56, 56]         --

  ...
  ...
  ...

├─AdaptiveAvgPool2d: 1-9                 [-1, 2048, 1, 1]          --
├─Linear: 1-10                           [-1, 1000]                2,049,000
==========================================================================================
Total params: 60,192,808
Trainable params: 60,192,808
Non-trainable params: 0
------------------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 344.16
Params size (MB): 229.62
Estimated Total Size (MB): 574.35
------------------------------------------------------------------------------------------

Other Examples

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1            [-1, 1, 16, 16]              10
              ReLU-2            [-1, 1, 16, 16]               0
            Conv2d-3            [-1, 1, 28, 28]              10
              ReLU-4            [-1, 1, 28, 28]               0
================================================================
Total params: 20
Trainable params: 20
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.77
Forward/backward pass size (MB): 0.02
Params size (MB): 0.00
Estimated Total Size (MB): 0.78
----------------------------------------------------------------

References

• Thanks to @sksq96, @nmhkahn, and @sangyx for providing the original code this project was based off of.
• For Model Size Estimation @jacobkimmel (details here)