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        "description": "[![Build Status](https://travis-ci.org/Swall0w/torchstat.svg?branch=master)](https://travis-ci.org/Swall0w/torchstat)\n\n# torchstat\nThis is a lightweight neural network analyzer based on PyTorch.\nIt is designed to make building your networks quick and easy, with the ability to debug them.\n**Note**: This repository is currently under development. Therefore, some APIs might be changed.\n\nThis tools can show\n\n* Total number of network parameters\n* Theoretical amount of floating point arithmetics (FLOPs)\n* Theoretical amount of multiply-adds (MAdd)\n* Memory usage\n\n## Installing\nThere're two ways to install torchstat into your environment.\n* Install it via pip.\n```bash\n$ pip install torchstat\n```\n\n* Install and update using **setup.py** after cloning this repository.\n```bash\n$ python3 setup.py install\n```\n\n## A Simple Example\nIf you want to run the torchstat asap, you can call it as a CLI tool if your network exists in a script.\nOtherwise you need to import torchstat as a module.\n\n### CLI tool\n```bash\n$ torchstat masato$ torchstat -f example.py -m Net\n[MAdd]: Dropout2d is not supported!\n[Flops]: Dropout2d is not supported!\n[Memory]: Dropout2d is not supported!\n      module name  input shape output shape     params memory(MB)           MAdd         Flops  MemRead(B)  MemWrite(B) duration[%]   MemR+W(B)\n0           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\n1           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\n2      conv2_drop   20 106 106   20 106 106        0.0       0.86            0.0           0.0         0.0          0.0       4.09%         0.0\n3             fc1        56180           50  2809050.0       0.00    5,617,950.0   2,809,000.0  11460920.0        200.0      11.58%  11461120.0\n4             fc2           50           10      510.0       0.00          990.0         500.0      2240.0         40.0       0.22%      2280.0\ntotal                                        2815340.0       3.56  190,578,940.0  95,998,220.0      2240.0         40.0     100.00%  15407512.0\n===============================================================================================================================================\nTotal params: 2,815,340\n-----------------------------------------------------------------------------------------------------------------------------------------------\nTotal memory: 3.56MB\nTotal MAdd: 190.58MMAdd\nTotal Flops: 96.0MFlops\nTotal MemR+W: 14.69MB\n```\n\nIf you're not sure how to use a specific command, run the command with the -h or \u2013help switches.\nYou'll see usage information and a list of options you can use with the command.\n\n### Module\n```python\nfrom torchstat import stat\nimport torchvision.models as models\n\nmodel = models.resnet18()\nstat(model, (3, 224, 224))\n```\n\n## Features & TODO\n**Note**: These features work only nn.Module. Modules in torch.nn.functional are not supported yet.\n- [x] FLOPs\n- [x] Number of Parameters\n- [x] Total memory\n- [x] Madd(FMA)\n- [x] MemRead\n- [x] MemWrite\n- [ ] Model summary(detail, layer-wise)\n- [ ] Export score table\n- [ ] Arbitrary input shape\n\nFor the supported layers, check out [the details](./detail.md).\n\n\n## Requirements\n* Python 3.6+\n* Pytorch 0.4.0+\n* Pandas 0.23.4+\n* NumPy 1.14.3+\n\n## References\nThanks to @sovrasov for the initial version of flops computation, @ceykmc for the backbone of scripts.\n* [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch)\n* [pytorch_model_summary](https://github.com/ceykmc/pytorch_model_summary)\n* [chainer_computational_cost](https://github.com/belltailjp/chainer_computational_cost)\n* [convnet-burden](https://github.com/albanie/convnet-burden).\n\n\n",
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