torch-summary 1.4.2

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. This project addresses all of the issues and pull requests left on the original projects by introducing a completely new API.

This version now supports:

• RNNs, LSTMs, and other recursive layers
• Sequentials & Module Lists
• Branching output used to explore model layers using specified depths
• Returns ModelStatistics object containing all summary data fields
• Configurable columns

Other new features:

• Verbose mode to show weights and bias layers
• Accepts either input data or simply the input shape!
• Customizable widths and batch dimension
• Comprehensive unit/output testing, linting, and code coverage testing

Usage

pip install torch-summary

or

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

How To Use

from torchsummary import summary

model = ConvNet()
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
==========================================================================================

Documentation

"""
Summarize the given PyTorch model. Summarized information includes:
    1) Layer names,
    2) input/output shapes,
    3) kernel shape,
    4) # of parameters,
    5) # of operations (Mult-Adds)

Args:
    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). Should NOT include batch size in the tuple.
            - OR -
            If input_data is not provided, no forward pass through the network is performed,
            and the provided model information is limited to layer names.

    batch_dim (int):
            Batch_dimension of input data. Default: 0

    branching (bool):
            Whether to use the branching layout for the printed output. Default: True

    col_names (Sequence[str]):
            Specify which columns to show in the output. Currently supported:
                    ("input_size", "output_size", "num_params", "kernel_size", "mult_adds")
            If input_data is not provided, only "num_params" is used.
            Default: ("output_size", "num_params")

    col_width (int):
            Width of each column. Default: 25

    depth (int):
            Number of nested layers to traverse (e.g. Sequentials). Default: 3

    device (torch.Device):
            Uses this torch device for model and input_data.
            If not specified, uses result of torch.cuda.is_available(). Default: None

    dtypes (List[torch.dtype]):
            For multiple inputs, specify the size of both inputs, and
            also specify the types of each parameter here. Default: None

    verbose (int):
            0 (quiet): No output
            1 (default): Print model summary
            2 (verbose): Show weight and bias layers in full detail
            Default: 1

    *args, **kwargs:
            Other arguments used in `model.forward` function.

Return:
    ModelStatistics object
            See torchsummary/model_statistics.py for more information.
"""

Examples

Get Model Summary as String

from torchsummary import summary

model_stats = summary(your_model, (3, 28, 28), verbose=0)
summary_str = str(model_stats)
# summary_str contains the string representation of the summary. See below for examples.

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
Total mult-adds (G): 11.63
==========================================================================================
Input size (MB): 0.57
Forward/backward pass size (MB): 344.16
Params size (MB): 229.62
Estimated Total Size (MB): 574.35
==========================================================================================

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):
    """ Batch-first LSTM model. """
    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, batch_first=True)
        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                                --                   [-1, 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
Total mult-adds (M): 3.78
========================================================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 1.03
Params size (MB): 14.44
Estimated Total Size (MB): 15.46
========================================================================================================================

Sequentials & ModuleLists

class ContainerModule(nn.Module):
    """ Model using ModuleList. """

    def __init__(self):
        super().__init__()
        self._layers = nn.ModuleList()
        self._layers.append(nn.Linear(5, 5))
        self._layers.append(ContainerChildModule())
        self._layers.append(nn.Linear(5, 5))

    def forward(self, x):
        for layer in self._layers:
            x = layer(x)
        return x


class ContainerChildModule(nn.Module):
    """ Model using Sequential in different ways. """

    def __init__(self):
        super().__init__()
        self._sequential = nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5))
        self._between = nn.Linear(5, 5)

    def forward(self, x):
        out = self._sequential(x)
        out = self._between(out)
        for l in self._sequential:
            out = l(out)

        out = self._sequential(x)
        for l in self._sequential:
            out = l(out)
        return out

summary(ContainerModule(), (5,))

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─ModuleList: 1                          []                        --
|    └─Linear: 2-1                       [-1, 5]                   30
|    └─ContainerChildModule: 2-2         [-1, 5]                   --
|    |    └─Sequential: 3-1              [-1, 5]                   --
|    |    |    └─Linear: 4-1             [-1, 5]                   30
|    |    |    └─Linear: 4-2             [-1, 5]                   30
|    |    └─Linear: 3-2                  [-1, 5]                   30
|    |    └─Sequential: 3                []                        --
|    |    |    └─Linear: 4-3             [-1, 5]                   (recursive)
|    |    |    └─Linear: 4-4             [-1, 5]                   (recursive)
|    |    └─Sequential: 3-3              [-1, 5]                   (recursive)
|    |    |    └─Linear: 4-5             [-1, 5]                   (recursive)
|    |    |    └─Linear: 4-6             [-1, 5]                   (recursive)
|    |    |    └─Linear: 4-7             [-1, 5]                   (recursive)
|    |    |    └─Linear: 4-8             [-1, 5]                   (recursive)
|    └─Linear: 2-3                       [-1, 5]                   30
==========================================================================================
Total params: 150
Trainable params: 150
Non-trainable params: 0
Total mult-adds (M): 0.00
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
==========================================================================================

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
================================================================

Contributing

All issues and pull requests are much appreciated! If you are wondering how to build the project:

• torch-summary is actively developed using Python 3.7+.
  • Changes should be backward compatible with Python 3.5, but this is subject to change in the future.
  • First, be sure to run ./scripts/install-hooks
  • To run all tests and use auto-formatting tools, check out scripts/run-tests.
  • To only run unit tests, run pytest.

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)