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

Results of Recent Models

The implementation are adapted from torchvision. Following results can be obtained using benchmark/evaluate_famours_models.py.

Model	Params(M)	MACs(G)
alexnet	58.27	0.72
vgg11	126.71	7.21
vgg11_bn	126.71	7.24
vgg13	126.88	10.66
vgg13_bn	126.89	10.70
vgg16	131.95	14.54
vgg16_bn	131.96	14.59
vgg19	137.01	18.41
vgg19_bn	137.02	18.47
resnet18	11.15	1.70
resnet34	20.79	3.43
resnet50	24.37	3.85
resnet101	42.49	7.33
resnet152	57.40	10.81
wide_resnet101_2	121.01	21.27
wide_resnet50_2	65.69	10.67

Model	Params(M)	MACs(G)
resnext101_32x8d	84.68	15.41
resnext50_32x4d	23.87	4.00
densenet121	7.61	2.70
densenet161	27.35	7.31
densenet169	13.49	3.20
densenet201	19.09	4.09
squeezenet1_0	1.19	0.77
squeezenet1_1	1.18	0.33
mnasnet0_5	2.12	0.13
mnasnet0_75	3.02	0.23
mnasnet1_0	4.18	0.31
mnasnet1_3	5.99	0.49
mobilenet_v2	3.34	0.31
shufflenet_v2_x0_5	1.30	0.04
shufflenet_v2_x1_0	2.17	0.14
shufflenet_v2_x1_5	3.34	0.29
shufflenet_v2_x2_0	7.05	0.56

Metadata

Release files for thop 0.0.31-1909021322

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 0.0.31-1909021322
File	Interpreter	ABI	Platform	Reset
thop-0.0.31.post1909021322-py3-none-any.whl 6.3 kB Sep 2, 2019	Python 3	none	any	Details

Release files / thop-0.0.31.post1909021322-py3-none-any.whl

Download URL	thop-0.0.31.post1909021322-py3-none-any.whl
Size	6.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`430628c7b8e808890ec821b8663f8841f36539f48f39e7e487905c1b823f3f9a`
BLAKE2b-256 checksum How to use checksums	`d01c120d38808b6ae56d90ece302e45e133f213c5aaf203a761700251c414c22`
Upload date	Sep 2, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3`