ultralytics-thop 2.0.8

Description:

ultralyticsthop 2.0.8

🚀 THOP: PyTorch-OpCounter
Welcome to the THOP repository, your comprehensive solution for profiling PyTorch models by computing the number of Multiply-Accumulate Operations (MACs) and parameters. This tool is essential for deep learning practitioners to evaluate model efficiency and performance.

📄 Description
THOP offers an intuitive API to profile PyTorch models by calculating the number of MACs and parameters. This functionality is crucial for assessing the computational efficiency and memory footprint of deep learning models.
📦 Installation
You can install THOP via pip:

pip install ultralytics-thop

Alternatively, install the latest version directly from GitHub:
pip install --upgrade git+https://github.com/ultralytics/thop.git

🛠 How to Use
Basic Usage
To profile a model, you can use the following example:
import torch
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 Custom Rules for Third-Party Modules
You can define custom rules for unsupported modules:
import torch.nn as nn


class YourModule(nn.Module):
# your definition
pass


def count_your_model(model, x, y):
# your rule here
pass


input = torch.randn(1, 3, 224, 224)
macs, params = profile(model, inputs=(input,), custom_ops={YourModule: count_your_model})

Improve Output Readability
Use thop.clever_format for a more readable output:
from thop import clever_format

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

📊 Results of Recent Models
The following table presents the parameters and MACs for popular models. These results can be reproduced using the script 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






💡 Contribute
We welcome community contributions to enhance THOP. Please check our Contributing Guide for more details. Your feedback and suggestions are highly appreciated!
📄 License
THOP is licensed under the AGPL-3.0 License. For more information, see the LICENSE file.
📮 Contact
For bugs or feature requests, please open an issue on GitHub Issues. Join our community on Discord for discussions and support.