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 installIf 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 masato$ torchstat -f example.py -m Net
[MAdd]: Dropout2d is not supported!
[Flops]: Dropout2d is not supported!
[Memory]: Dropout2d is not supported!
module name input shape output shape params memory(MB) MAdd Flops MemRead(B) MemWrite(B) duration[%] MemR+W(B)
0 conv1 3 224 224 10 220 220 760.0 1.85 72,600,000.0 36,784,000.0 605152.0 1936000.0 57.49% 2541152.0
1 conv2 10 110 110 20 106 106 5020.0 0.86 112,360,000.0 56,404,720.0 504080.0 898880.0 26.62% 1402960.0
2 conv2_drop 20 106 106 20 106 106 0.0 0.86 0.0 0.0 0.0 0.0 4.09% 0.0
3 fc1 56180 50 2809050.0 0.00 5,617,950.0 2,809,000.0 11460920.0 200.0 11.58% 11461120.0
4 fc2 50 10 510.0 0.00 990.0 500.0 2240.0 40.0 0.22% 2280.0
total 2815340.0 3.56 190,578,940.0 95,998,220.0 2240.0 40.0 100.00% 15407512.0
===============================================================================================================================================
Total params: 2,815,340
-----------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 3.56MB
Total MAdd: 190.58MMAdd
Total Flops: 96.0MFlops
Total MemR+W: 14.69MBIf 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)
- MemRead
- MemWrite
- Model summary(detail, layer-wise)
- Export score table
- Arbitrary input shape
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.