https://github.com/Lyken17/pytorch-OpCounter

Count the MACs / FLOPs of your PyTorch model.
https://github.com/Lyken17/pytorch-OpCounter

Last synced: 2 months ago
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Count the MACs / FLOPs of your PyTorch model.

• Host: GitHub
• URL: https://github.com/Lyken17/pytorch-OpCounter
• Owner: Lyken17
• License: mit
• Created: 2018-01-26T06:20:22.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-07-08T06:44:15.000Z (6 months ago)
• Last Synced: 2024-07-28T11:32:59.221Z (6 months ago)
• Language: Python
• Homepage:
• Size: 173 KB
• Stars: 4,796
• Watchers: 30
• Forks: 521
• Open Issues: 82
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-pytorch-list-CNVersion - pytorch-OpCounter
• Awesome-pytorch-list - pytorch-OpCounter

README

        
# THOP: PyTorch-OpCounter

## How to install 

    

`pip install thop` (now continously intergrated on [Github actions](https://github.com/features/actions))

OR

`pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git`

    

## How to use 

* Basic usage 

    ```python

    from torchvision.models import resnet50

    from thop import profile

    model = resnet50()

    input = torch.randn(1, 3, 224, 224)

    macs, params = profile(model, inputs=(input, ))

    ```    

* Define the rule for 3rd party module.

    ```python

    class YourModule(nn.Module):

        # your definition

    def count_your_model(model, x, y):

        # your rule here

    

    input = torch.randn(1, 3, 224, 224)

    macs, params = profile(model, inputs=(input, ), 

                            custom_ops={YourModule: count_your_model})

    ```

    

* Improve the output readability

    Call `thop.clever_format` to give a better format of the output.

    ```python

    from thop import clever_format

    macs, params = clever_format([macs, params], "%.3f")

    ```    

    

## Results of Recent Models

The implementation are adapted from `torchvision`. Following results can be obtained using [benchmark/evaluate_famous_models.py](benchmark/evaluate_famous_models.py).





Model | Params(M) | MACs(G)

---|---|---

alexnet | 61.10 | 0.77

vgg11 | 132.86 | 7.74

vgg11_bn | 132.87 | 7.77

vgg13 | 133.05 | 11.44

vgg13_bn | 133.05 | 11.49

vgg16 | 138.36 | 15.61

vgg16_bn | 138.37 | 15.66

vgg19 | 143.67 | 19.77

vgg19_bn | 143.68 | 19.83

resnet18 | 11.69 | 1.82

resnet34 | 21.80 | 3.68

resnet50 | 25.56 | 4.14

resnet101 | 44.55 | 7.87

resnet152 | 60.19 | 11.61

wide_resnet101_2 | 126.89 | 22.84

wide_resnet50_2 | 68.88 | 11.46

Model | Params(M) | MACs(G)

---|---|---

resnext50_32x4d | 25.03 | 4.29

resnext101_32x8d | 88.79 | 16.54

densenet121 | 7.98 | 2.90

densenet161 | 28.68 | 7.85

densenet169 | 14.15 | 3.44

densenet201 | 20.01 | 4.39

squeezenet1_0 | 1.25 | 0.82

squeezenet1_1 | 1.24 | 0.35

mnasnet0_5 | 2.22 | 0.14

mnasnet0_75 | 3.17 | 0.24

mnasnet1_0 | 4.38 | 0.34

mnasnet1_3 | 6.28 | 0.53

mobilenet_v2 | 3.50 | 0.33

shufflenet_v2_x0_5 | 1.37 | 0.05

shufflenet_v2_x1_0 | 2.28 | 0.15

shufflenet_v2_x1_5 | 3.50 | 0.31

shufflenet_v2_x2_0 | 7.39 | 0.60

inception_v3 | 27.16 | 5.75