Package: torch-summary
torchinfoAnnouncement: We have moved totorchinfo!torch-summaryhas been renamed totorchinfo! Nearly all of the functionality is the same, but the new name will allow us to develop and experiment with additional new features. All links now redirect totorchinfo, so please leave an issue there if you have any questions.Thetorch-summarypackage will continue to exist for the foreseeable future, so please feel free to pin your desired version (1.4.3for Python 3.5,1.4.4+for everything else), or try outtorchinfo. Thanks!torch-summaryTorch-summary provides information complementary to what is provided byprint(your_model)in PyTorch, similar to Tensorflow'smodel.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.Usagepip install torch-summaryHow To Usefromtorchsummaryimportsummarymodel=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
==========================================================================================This version now supports:RNNs, LSTMs, and other recursive layersSequentials & Module ListsBranching output used to explore model layers using specified depthsReturns ModelStatistics object containing all summary data fieldsConfigurable columnsOther new features:Verbose mode to show weights and bias layersAccepts either input data or simply the input shape!Customizable widths and batch dimensionComprehensive unit/output testing, linting, and code coverage testingDocumentation"""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. The model should be fully in either train()or eval() mode. If layers are not all in the same mode, running summarymay have side effects on batchnorm or dropout statistics. If youencounter an issue with this, please open a GitHub issue.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).You should NOT include batch size in the tuple.- OR -If input_data is not provided, no forward pass through the network isperformed, and the provided model information is limited to layer names.Default: Nonebatch_dim (int):Batch_dimension of input data. If batch_dim is None, the input datais assumed to contain the batch dimension.WARNING: in a future version, the default will change to None.Default: 0branching (bool):Whether to use the branching layout for the printed output.Default: Truecol_names (Iterable[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: 25depth (int):Number of nested layers to traverse (e.g. Sequentials).Default: 3device (torch.Device):Uses this torch device for model and input_data.If not specified, uses result of torch.cuda.is_available().Default: Nonedtypes (List[torch.dtype]):For multiple inputs, specify the size of both inputs, andalso specify the types of each parameter here.Default: Noneverbose (int):0 (quiet): No output1 (default): Print model summary2 (verbose): Show weight and bias layers in full detailDefault: 1*args, **kwargs:Other arguments used in `model.forward` function.Return:ModelStatistics objectSee torchsummary/model_statistics.py for more information."""ExamplesGet Model Summary as Stringfromtorchsummaryimportsummarymodel_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.ResNetimporttorchvisionmodel=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 TypesclassMultipleInputNetDifferentDtypes(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)defforward(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)returnF.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 ConfigurationsclassLSTMNet(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_dimself.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)defforward(self,x):embed=self.embedding(x)out,hidden=self.encoder(embed)out=self.decoder(out)out=out.view(-1,out.size(2))returnout,hiddensummary(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 & ModuleListsclassContainerModule(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))defforward(self,x):forlayerinself._layers:x=layer(x)returnxclassContainerChildModule(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)defforward(self,x):out=self._sequential(x)out=self._between(out)forlinself._sequential:out=l(out)out=self._sequential(x)forlinself._sequential:out=l(out)returnoutsummary(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
================================================================Future PlansChange project name totorchinfo. The API will eventually mature to the point that this project deserves its own name.Support all types of inputs - showing tuples and dict inputs cleanly rather than only using the first tensor in the list.Defaultbatch_dimtoNonerather than0. Users must specify the batch size in the input shape, or pass inbatch_dim=0in order to ignore it.FunctionalNet unused; figure out a way to hook into functional layers.ContributingAll issues and pull requests are much appreciated! If you are wondering how to build the project:torch-summary is actively developed using the lastest version of Python.Changes should be backward compatible with Python 3.6, but this is subject to change in the future.Runpip install -r requirements-dev.txt. We use the latest versions of all dev packages.First, be sure to run./scripts/install-hooksTo run all tests and use auto-formatting tools, check outscripts/run-tests.To only run unit tests, runpytest.ReferencesThanks to @sksq96, @nmhkahn, and @sangyx for providing the original code this project was based off of.For Model Size Estimation @jacobkimmel (details here)