Count the MACs / FLOPs of your PyTorch model.

Overview

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
Owner
Ligeng Zhu
Ph.D. student in [email protected], alumni at SFU and ZJU.
Ligeng Zhu
Dataset and Source code of paper 'Enhancing Keyphrase Extraction from Academic Articles with their Reference Information'.

Enhancing Keyphrase Extraction from Academic Articles with their Reference Information Overview Dataset and code for paper "Enhancing Keyphrase Extrac

15 Nov 24, 2022
Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference

Ankou Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering t

SoftSec Lab 54 Dec 24, 2022
Fully Convolutional Refined Auto Encoding Generative Adversarial Networks for 3D Multi Object Scenes

Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes This repository contains the source code for Full

Yu Nishimura 106 Nov 21, 2022
Efficient Speech Processing Tookit for Automatic Speaker Recognition

Sugar Efficient Speech Processing Tookit for Automatic Speaker Recognition | HuggingFace | What's New EfficientTDNN: Efficient Architecture Search for

WangRui 14 Sep 14, 2022
A program that can analyze videos according to the weights you select

MaskMonitor A program that can analyze videos according to the weights you select 下載 訓練完的 weight檔案 執行 MaskDetection.py 內部可更改 輸入來源(鏡頭, 影片, 圖片) 以及輸出條件(人

Patrick_star 1 Nov 07, 2021
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

Sayed Hashim 3 Nov 15, 2022
EMNLP'2021: SimCSE: Simple Contrastive Learning of Sentence Embeddings

SimCSE: Simple Contrastive Learning of Sentence Embeddings This repository contains the code and pre-trained models for our paper SimCSE: Simple Contr

Princeton Natural Language Processing 2.5k Dec 29, 2022
A time series processing library

Timeseria Timeseria is a time series processing library which aims at making it easy to handle time series data and to build statistical and machine l

Stefano Alberto Russo 11 Aug 08, 2022
Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

Low-light Image Enhancement via Breaking Down the Darkness by Qiming Hu, Xiaojie Guo. 1. Dependencies Python3 PyTorch=1.0 OpenCV-Python, TensorboardX

Qiming Hu 30 Jan 01, 2023
Kaggle Feedback Prize - Evaluating Student Writing 15th solution

Kaggle Feedback Prize - Evaluating Student Writing 15th solution First of all, I would like to thank the excellent notebooks and discussions from http

Lingyuan Zhang 6 Mar 24, 2022
On Effective Scheduling of Model-based Reinforcement Learning

On Effective Scheduling of Model-based Reinforcement Learning Code to reproduce the experiments in On Effective Scheduling of Model-based Reinforcemen

laihang 8 Oct 07, 2022
Generalized Random Forests

generalized random forests A pluggable package for forest-based statistical estimation and inference. GRF currently provides non-parametric methods fo

GRF Labs 781 Dec 25, 2022
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis

OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis Overview OpenABC-D is a large-scale labeled dataset generate

NYU Machine-Learning guided Design Automation (MLDA) 31 Nov 22, 2022
3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

SynergyNet 3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry Cho-Ying Wu, Qiangeng Xu, Ulrich Neumann, CGIT Lab at Unive

Cho-Ying Wu 239 Jan 06, 2023
一个多模态内容理解算法框架,其中包含数据处理、预训练模型、常见模型以及模型加速等模块。

Overview 架构设计 插件介绍 安装使用 框架简介 方便使用,支持多模态,多任务的统一训练框架 能力列表: bert + 分类任务 自定义任务训练(插件注册) 框架设计 框架采用分层的思想组织模型训练流程。 DATA 层负责读取用户数据,根据 field 管理数据。 Parser 层负责转换原

Tencent 265 Dec 22, 2022
FANet - Real-time Semantic Segmentation with Fast Attention

FANet Real-time Semantic Segmentation with Fast Attention Ping Hu, Federico Perazzi, Fabian Caba Heilbron, Oliver Wang, Zhe Lin, Kate Saenko , Stan Sc

Ping Hu 42 Nov 30, 2022
Indonesian Car License Plate Character Recognition using Tensorflow, Keras and OpenCV.

Monopol Indonesian Car License Plate (Indonesia Mobil Nomor Polisi) Character Recognition using Tensorflow, Keras and OpenCV. Background This applicat

Jayaku Briliantio 3 Apr 07, 2022
Deploy optimized transformer based models on Nvidia Triton server

Deploy optimized transformer based models on Nvidia Triton server

Lefebvre Sarrut Services 1.2k Jan 05, 2023
Sdf sparse conv - Deep Learning on SDF for Classifying Brain Biomarkers

Deep Learning on SDF for Classifying Brain Biomarkers To reproduce the results f

1 Jan 25, 2022
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022