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THOP: PyTorch-OpCounter

How to install

pip install thop (now continously intergrated on Github actions)

OR

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

How to use

  • Basic usage

    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.

    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.

    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.

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

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Count the MACs / FLOPs of your PyTorch model.

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