Awesome
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 |