This is a lightweight neural network analyzer based on PyTorch. It is designed to make building your networks quick and easy, with the ability to debug them. Note: This repository is currently under development. Therefore, some APIs might be changed.

This tools can show

• Total number of network parameters
• Theoretical amount of floating point arithmetics (FLOPs)
• Theoretical amount of multiply-adds (MAdd)
• Memory usage

There're two ways to install torchstat into your environment.

• Install it via pip.

$ pip install torchstat

• Install and update using setup.py after cloning this repository.

$ python3 setup.py install

If you want to run the torchstat asap, you can call it as a CLI tool if your network exists in a script. Otherwise you need to import torchstat as a module.

$ torchstat --file example.py --model Net
[MAdd]: Dropout2d is not supported!
[Flops]: Dropout2d is not supported!
      module name  input shape output shape     params memory(MB)           MAdd         Flops duration[%]
0           conv1    3 224 224   10 220 220      760.0       1.85   72,600,000.0  36,784,000.0      60.11%
1           conv2   10 110 110   20 106 106     5020.0       0.86  112,360,000.0  56,404,720.0      35.08%
2      conv2_drop   20 106 106   20 106 106        0.0       0.86            0.0           0.0       0.34%
3             fc1        56180           50  2809050.0       0.00    5,617,950.0   2,809,000.0       4.25%
4             fc2           50           10      510.0       0.00          990.0         500.0       0.22%
total                                        2815340.0       3.56  190,578,940.0  95,998,220.0     100.00%
==========================================================================================================
Total params: 2,815,340
----------------------------------------------------------------------------------------------------------
Total memory: 3.56MB
Total MAdd: 190.58MMAdd
Total Flops: 96.0MFlops

If you're not sure how to use a specific command, run the command with the -h or –help switches. You'll see usage information and a list of options you can use with the command.

from torchstat import stat
import torchvision.models as models

model = models.resnet18()
stat(model, (3, 224, 224))

Note: These features work only nn.Module. Modules in torch.nn.functional are not supported yet.

• FLOPs
• Number of Parameters
• Total memory
• Madd(FMA)
• Model summary(detail, layer-wise)
• Export score table
• MemRead
• MemWrite

For the supported layers, check out the details.

• Python 3.6+
• Pytorch 0.4.0+
• Pandas 0.23.4+
• NumPy 1.14.3+

Thanks to @sovrasov for the initial version of flops computation, @ceykmc for the backbone of scripts.

• flops-counter.pytorch
• pytorch_model_summary
• chainer_computational_cost
• convnet-burden.