Package: torchinfo
torchinfo(formerly torch-summary)Torchinfo 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.Supports PyTorch versions 1.4.0+.Usagepip install torchinfoAlternatively, via conda:conda install -c conda-forge torchinfoHow To Usefromtorchinfoimportsummarymodel=ConvNet()batch_size=16summary(model,input_size=(batch_size,1,28,28))================================================================================================================
Layer (type:depth-idx)          Input Shape          Output Shape         Param #            Mult-Adds
================================================================================================================
SingleInputNet                  [7, 1, 28, 28]       [7, 10]              --                 --
├─Conv2d: 1-1                   [7, 1, 28, 28]       [7, 10, 24, 24]      260                1,048,320
├─Conv2d: 1-2                   [7, 10, 12, 12]      [7, 20, 8, 8]        5,020              2,248,960
├─Dropout2d: 1-3                [7, 20, 8, 8]        [7, 20, 8, 8]        --                 --
├─Linear: 1-4                   [7, 320]             [7, 50]              16,050             112,350
├─Linear: 1-5                   [7, 50]              [7, 10]              510                3,570
================================================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 3.41
================================================================================================================
Input size (MB): 0.02
Forward/backward pass size (MB): 0.40
Params size (MB): 0.09
Estimated Total Size (MB): 0.51
================================================================================================================Note: if you are using a Jupyter Notebook or Google Colab,summary(model, ...)must be the returned value of the cell.
If it is not, you should wrap the summary in a print(), e.g.print(summary(model, ...)).
Seetests/jupyter_test.ipynbfor examples.This version now supports:RNNs, LSTMs, and other recursive layersBranching output used to explore model layers using specified depthsReturns ModelStatistics object containing all summary data fieldsConfigurable rows/columnsJupyter Notebook / Google ColabOther new features:Verbose mode to show weights and bias layersAccepts either input data or simply the input shape!Customizable line widths and batch dimensionComprehensive unit/output testing, linting, and code coverage testingCommunity Contributions:Sequentials & ModuleLists (thanks to @roym899)Improved Mult-Add calculations (thanks to @TE-StefanUhlich, @zmzhang2000)Dict/Misc input data (thanks to @e-dorigatti)Pruned layer support (thanks to @MajorCarrot)Documentationdefsummary(model:nn.Module,input_size:Optional[INPUT_SIZE_TYPE]=None,input_data:Optional[INPUT_DATA_TYPE]=None,batch_dim:Optional[int]=None,cache_forward_pass:Optional[bool]=None,col_names:Optional[Iterable[str]]=None,col_width:int=25,depth:int=3,device:Optional[torch.device]=None,dtypes:Optional[List[torch.dtype]]=None,mode:str|None=None,row_settings:Optional[Iterable[str]]=None,verbose:int=1,**kwargs:Any,)->ModelStatistics:"""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),6) whether layer is trainableNOTE: If neither input_data or input_size are provided, no forward pass through thenetwork is performed, and the provided model information is limited to layer names.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_size (Sequence of Sizes):Shape of input data as a List/Tuple/torch.Size(dtypes must match model input, default is FloatTensors).You should include batch size in the tuple.Default: Noneinput_data (Sequence of Tensors):Arguments for the model's forward pass (dtypes inferred).If the forward() function takes several parameters, pass in a list ofargs or a dict of kwargs (if your forward() function takes in a dictas its only argument, wrap it in a list).Default: Nonebatch_dim (int):Batch_dimension of input data. If batch_dim is None, assumeinput_data / input_size contains the batch dimension, which is usedin all calculations. Else, expand all tensors to contain the batch_dim.Specifying batch_dim can be an runtime optimization, since if batch_dimis specified, torchinfo uses a batch size of 1 for the forward pass.Default: Nonecache_forward_pass (bool):If True, cache the run of the forward() function using the modelclass name as the key. If the forward pass is an expensive operation,this can make it easier to modify the formatting of your modelsummary, e.g. changing the depth or enabled column types, especiallyin Jupyter Notebooks.WARNING: Modifying the model architecture or input data/input size whenthis feature is enabled does not invalidate the cache or re-run theforward pass, and can cause incorrect summaries as a result.Default: Falsecol_names (Iterable[str]):Specify which columns to show in the output. Currently supported: ("input_size","output_size","num_params","params_percent","kernel_size","mult_adds","trainable",)Default: ("output_size", "num_params")If input_data / input_size are not provided, only "num_params" is used.col_width (int):Width of each column.Default: 25depth (int):Depth of nested layers to display (e.g. Sequentials).Nested layers below this depth will not be displayed in the summary.Default: 3device (torch.Device):Uses this torch device for model and input_data.If not specified, uses the dtype of input_data if given, or theparameters of the model. Otherwise, uses the result oftorch.cuda.is_available().Default: Nonedtypes (List[torch.dtype]):If you use input_size, torchinfo assumes your input uses FloatTensors.If your model use a different data type, specify that dtype.For multiple inputs, specify the size of both inputs, andalso specify the types of each parameter here.Default: Nonemode (str)Either "train" or "eval", which determines whether we callmodel.train() or model.eval() before calling summary().Default: "eval".row_settings (Iterable[str]):Specify which features to show in a row. Currently supported: ("ascii_only","depth","var_names",)Default: ("depth",)verbose (int):0 (quiet): No output1 (default): Print model summary2 (verbose): Show weight and bias layers in full detailDefault: 1If using a Juypter Notebook or Google Colab, the default is 0.**kwargs:Other arguments used in `model.forward` function. Passing *args is nolonger supported.Return:ModelStatistics objectSee torchinfo/model_statistics.py for more information."""ExamplesGet Model Summary as Stringfromtorchinfoimportsummarymodel_stats=summary(your_model,(1,3,28,28),verbose=0)summary_str=str(model_stats)# summary_str contains the string representation of the summary!Explore Different ConfigurationsclassLSTMNet(nn.Module):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(),(1,100),dtypes=[torch.long],verbose=2,col_width=16,col_names=["kernel_size","output_size","num_params","mult_adds"],row_settings=["var_names"],)========================================================================================================================
Layer (type (var_name))                  Kernel Shape         Output Shape         Param #              Mult-Adds
========================================================================================================================
LSTMNet (LSTMNet)                        --                   [100, 20]            --                   --
├─Embedding (embedding)                  --                   [1, 100, 300]        6,000                6,000
│    └─weight                            [300, 20]                                 └─6,000
├─LSTM (encoder)                         --                   [1, 100, 512]        3,768,320            376,832,000
│    └─weight_ih_l0                      [2048, 300]                               ├─614,400
│    └─weight_hh_l0                      [2048, 512]                               ├─1,048,576
│    └─bias_ih_l0                        [2048]                                    ├─2,048
│    └─bias_hh_l0                        [2048]                                    ├─2,048
│    └─weight_ih_l1                      [2048, 512]                               ├─1,048,576
│    └─weight_hh_l1                      [2048, 512]                               ├─1,048,576
│    └─bias_ih_l1                        [2048]                                    ├─2,048
│    └─bias_hh_l1                        [2048]                                    └─2,048
├─Linear (decoder)                       --                   [1, 100, 20]         10,260               10,260
│    └─weight                            [512, 20]                                 ├─10,240
│    └─bias                              [20]                                      └─20
========================================================================================================================
Total params: 3,784,580
Trainable params: 3,784,580
Non-trainable params: 0
Total mult-adds (M): 376.85
========================================================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.67
Params size (MB): 15.14
Estimated Total Size (MB): 15.80
========================================================================================================================ResNetimporttorchvisionmodel=torchvision.models.resnet152()summary(model,(1,3,224,224),depth=3)==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
ResNet                                   [1, 1000]                 --
├─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]          16,896
│    │    └─ReLU: 3-10                   [1, 256, 56, 56]          --
│    └─Bottleneck: 2-2                   [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): 11.51
==========================================================================================
Input size (MB): 0.60
Forward/backward pass size (MB): 360.87
Params size (MB): 240.77
Estimated Total Size (MB): 602.25
==========================================================================================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
torchinfo 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=[input_data,other_input_data,...])Sequentials & ModuleListsclassContainerModule(nn.Module):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):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(),(1,5))==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
ContainerModule                          [1, 5]                    --
├─ModuleList: 1-1                        --                        --
│    └─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: 3-3              --                        (recursive)
│    │    │    └─Linear: 4-3             [1, 5]                    (recursive)
│    │    │    └─Linear: 4-4             [1, 5]                    (recursive)
│    │    └─Sequential: 3-4              [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
==========================================================================================ContributingAll issues and pull requests are much appreciated! If you are wondering how to build the project:torchinfo is actively developed using the lastest version of Python.Changes should be backward compatible to Python 3.7, and will follow Python's End-of-Life guidance for old versions.Runpip install -r requirements-dev.txt. We use the latest versions of all dev packages.Runpre-commit install.To use auto-formatting tools, usepre-commit run -a.To run unit tests, runpytest.To update the expected output files, runpytest --overwrite.To skip output file tests, usepytest --no-outputReferencesThanks to @sksq96, @nmhkahn, and @sangyx for providing the inspiration for this project.For Model Size Estimation @jacobkimmel (details here)