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https://github.com/swall0w/torchstat
Model analyzer in PyTorch
https://github.com/swall0w/torchstat
python pytorch
Last synced: about 1 month ago
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Model analyzer in PyTorch
- Host: GitHub
- URL: https://github.com/swall0w/torchstat
- Owner: Swall0w
- License: mit
- Created: 2018-10-26T14:36:14.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-19T03:13:59.000Z (over 1 year ago)
- Last Synced: 2024-09-27T20:04:33.350Z (about 2 months ago)
- Topics: python, pytorch
- Language: Python
- Homepage:
- Size: 46.9 KB
- Stars: 1,460
- Watchers: 12
- Forks: 144
- Open Issues: 43
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://travis-ci.org/Swall0w/torchstat)
# torchstat
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
## Installing
There're two ways to install torchstat into your environment.
* Install it via pip.
```bash
$ pip install torchstat
```
* Install and update using **setup.py** after cloning this repository.
```bash
$ python3 setup.py install
```
## A Simple Example
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.
### CLI tool
```bash
$ 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: 14.69MB
```
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.
### Module
```python
from torchstat import stat
import torchvision.models as models
model = models.resnet18()
stat(model, (3, 224, 224))
```
## Features & TODO
**Note**: These features work only nn.Module. Modules in torch.nn.functional are not supported yet.
- [x] FLOPs
- [x] Number of Parameters
- [x] Total memory
- [x] Madd(FMA)
- [x] MemRead
- [x] MemWrite
- [ ] Model summary(detail, layer-wise)
- [ ] Export score table
- [ ] Arbitrary input shape
For the supported layers, check out [the details](./detail.md).
## Requirements
* Python 3.6+
* Pytorch 0.4.0+
* Pandas 0.23.4+
* NumPy 1.14.3+
## References
Thanks to @sovrasov for the initial version of flops computation, @ceykmc for the backbone of scripts.
* [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch)
* [pytorch_model_summary](https://github.com/ceykmc/pytorch_model_summary)
* [chainer_computational_cost](https://github.com/belltailjp/chainer_computational_cost)
* [convnet-burden](https://github.com/albanie/convnet-burden).