Metadata-Version: 2.1
Name: pthflops
Version: 0.3.3
Summary: Estimate FLOPs of neural networks
Home-page: https://github.com/1adrianb/pytorch-estimate-flops
Author: Adrian Bulat
Author-email: adrian@adrianbulat.com
License: BSD
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Requires-Dist: torch

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# pytorch-estimate-flops

Simple 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.

All contributions are welcomed.

## Installation

You can install the model using pip:

```bash
pip install pthflops
```
or directly from the github repository:
```bash
git clone https://github.com/1adrianb/pytorch-estimate-flops && pytorch-estimate-flops
python setup.py install
```

## Example

```python
import torch
from torchvision.models import resnet18

from pthflops import count_ops

# Create a network and a corresponding input
device = 'cuda:0'
model = resnet18().to(device)
inp = torch.rand(1,3,224,224).to(device)

# Count the number of FLOPs
count_ops(model, inp)
```

Ignoring certain layers:

```python
import torch
from torch import nn
from pthflops import count_ops

class CustomLayer(nn.Module):
    def __init__(self):
        super(CustomLayer, self).__init__()
        self.conv1 = nn.Conv2d(5, 5, 1, 1, 0)
        # ... other layers present inside will also be ignored

    def forward(self, x):
        return self.conv1(x)

# Create a network and a corresponding input
inp = torch.rand(1,5,7,7)
net = nn.Sequential(
    nn.Conv2d(5, 5, 1, 1, 0),
    nn.ReLU(inplace=True),
    CustomLayer()
)

# Count the number of FLOPs
count_ops(net, inp, ignore_layers=['CustomLayer'])

```


