THOP: PyTorch-OpCounter

How to install

pip install thop (now continously intergrated on Github actions)

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

Release files for thop 0.1.1.post2209072238

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for thop 0.1.1.post2209072238
File	Interpreter	ABI	Platform	Reset
thop-0.1.1.post2209072238-py3-none-any.whl 15.4 kB Sep 7, 2022	Python 3	none	any	Details

Release files / thop-0.1.1.post2209072238-py3-none-any.whl

Download URL	thop-0.1.1.post2209072238-py3-none-any.whl
Size	15.4 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`01473c225231927d2ad718351f78ebf7cffe6af3bed464c4f1ba1ef0f7cdda27`
BLAKE2b-256 checksum How to use checksums	`bb0f72beeab4ff5221dc47127c80f8834b4bcd0cb36f6ba91c0b1d04a1233403`
Upload date	Sep 7, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.6.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.8.5`