pip install thop
(now continously intergrated on Github actions)
OR
pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git
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")
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 |