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
pip install torch-summary
or
git clone https://github.com/tyleryep/torch-summary.git
from torchsummary import summary
summary(your_model, input_data)"""
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.
"""from torchsummary import summary
model_stats = summary(your_model, input_data=(C, H, W), verbose=0)
summary_str = str(model_stats)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
------------------------------------------------------------------------------------------
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, ...)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
--------------------------------------------------------------------------------------------------------
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
------------------------------------------------------------------------------------------
----------------------------------------------------------------
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
----------------------------------------------------------------
All issues and pull requests are much appreciated! If you are wondering how to build the project:
- The code in torchsummary/ contains Python 3.7+ features like f-strings and type annotations.
- To ensure compatibility with Python 3.5+, I build the project with all of these features stripped.
- See
torchsummary_build/pipbuildfor more details.
- See
- To run all tests and other auto-formatting tools, check out
scripts/run-tests. - To only run unit tests, run
pytest unit_test.
- Thanks to @sksq96, @nmhkahn, and @sangyx for providing the original code this project was based off of.
- For Model Size Estimation @jacobkimmel (details here)