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        "author": "Adrian Bulat",
        "author_email": "adrian@adrianbulat.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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        "description": "[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)  [![Build Status](https://travis-ci.com/1adrianb/pytorch-estimate-flops.svg?branch=master)](https://travis-ci.com/1adrianb/pytorch-estimate-flops)\n[![PyPI](https://img.shields.io/pypi/v/pthflops.svg?style=flat)](https://pypi.org/project/pthflops/)\n\n# pytorch-estimate-flops\n\nSimple pytorch utility that estimates the number of FLOPs for a given network. For now only some basic operations are supported (basically the ones I needed for my models). More will be added soon.\n\nAll contributions are welcomed.\n\n## Installation\n\nYou can install the model using pip:\n\n```bash\npip install pthflops\n```\nor directly from the github repository:\n```bash\ngit clone https://github.com/1adrianb/pytorch-estimate-flops && pytorch-estimate-flops\npython setup.py install\n```\n\n## Example\n\n```python\nimport torch\nfrom torchvision.models import resnet18\n\nfrom pthflops import count_ops\n\n# Create a network and a corresponding input\ndevice = 'cuda:0'\nmodel = resnet18().to(device)\ninp = torch.rand(1,3,224,224).to(device)\n\n# Count the number of FLOPs\ncount_ops(model, inp)\n```\n\nIgnoring certain layers:\n\n```python\nimport torch\nfrom torch import nn\nfrom pthflops import count_ops\n\nclass CustomLayer(nn.Module):\n    def __init__(self):\n        super(CustomLayer, self).__init__()\n        self.conv1 = nn.Conv2d(5, 5, 1, 1, 0)\n        # ... other layers present inside will also be ignored\n\n    def forward(self, x):\n        return self.conv1(x)\n\n# Create a network and a corresponding input\ninp = torch.rand(1,5,7,7)\nnet = nn.Sequential(\n    nn.Conv2d(5, 5, 1, 1, 0),\n    nn.ReLU(inplace=True),\n    CustomLayer()\n)\n\n# Count the number of FLOPs\ncount_ops(net, inp, ignore_layers=['CustomLayer'])\n\n```",
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