• Stars
    star
    428
  • Rank 101,481 (Top 2 %)
  • Language
    Python
  • License
    MIT License
  • Created over 5 years ago
  • Updated about 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

AutoML for Model Compression (AMC)

This repo contains the PyTorch implementation for paper AMC: AutoML for Model Compression and Acceleration on Mobile Devices.

overview

Reference

If you find the repo useful, please kindly cite our paper:

@inproceedings{he2018amc,
  title={AMC: AutoML for Model Compression and Acceleration on Mobile Devices},
  author={He, Yihui and Lin, Ji and Liu, Zhijian and Wang, Hanrui and Li, Li-Jia and Han, Song},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2018}
}

Other papers related to automated model design:

  • HAQ: Hardware-Aware Automated Quantization with Mixed Precision (CVPR 2019)

  • ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (ICLR 2019)

Training AMC

Current code base supports the automated pruning of MobileNet on ImageNet. The pruning of MobileNet consists of 3 steps: 1. strategy search; 2. export the pruned weights; 3. fine-tune from pruned weights.

To conduct the full pruning procedure, follow the instructions below (results might vary a little from the paper due to different random seed):

  1. Strategy Search

    To search the strategy on MobileNet ImageNet model, first get the pretrained MobileNet checkpoint on ImageNet by running:

    bash ./checkpoints/download.sh
    

    It will also download our 50% FLOPs compressed model. Then run the following script to search under 50% FLOPs constraint:

    bash ./scripts/search_mobilenet_0.5flops.sh

    Results may differ due to different random seed. The strategy we found and reported in the paper is:

    [3, 24, 48, 96, 80, 192, 200, 328, 352, 368, 360, 328, 400, 736, 752]
    
  2. Export the Pruned Weights

    After searching, we need to export the pruned weights by running:

    bash ./scripts/export_mobilenet_0.5flops.sh
    

    Also we need to modify MobileNet file to support the new pruned model (here it is already done in models/mobilenet.py)

  3. Fine-tune from Pruned Weightsa

    After exporting, we need to fine-tune from the pruned weights. For example, we can fine-tune using cosine learning rate for 150 epochs by running:

    bash ./scripts/finetune_mobilenet_0.5flops.sh
    

AMC Compressed Model

We also provide the models and weights compressed by our AMC method. We provide compressed MobileNet-V1 and MobileNet-V2 in both PyTorch and TensorFlow format here.

Detailed statistics are as follows:

Models Top1 Acc (%) Top5 Acc (%)
MobileNetV1-width*0.75 68.4 88.2
MobileNetV1-50%FLOPs 70.494 89.306
MobileNetV1-50%Time 70.200 89.430
MobileNetV2-width*0.75 69.8 89.6
MobileNetV2-70%FLOPs 70.854 89.914

Dependencies

Current code base is tested under following environment:

  1. Python 3.7.3
  2. PyTorch 1.1.0
  3. torchvision 0.2.1
  4. NumPy 1.14.3
  5. SciPy 1.1.0
  6. scikit-learn 0.19.1
  7. tensorboardX
  8. ImageNet dataset

Contact

To contact the authors:

Ji Lin, [email protected]

Song Han, [email protected]

More Repositories

1

streaming-llm

[ICLR 2024] Efficient Streaming Language Models with Attention Sinks
Python
6,530
star
2

bevfusion

[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Python
2,286
star
3

temporal-shift-module

[ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
Python
2,060
star
4

once-for-all

[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
Python
1,866
star
5

llm-awq

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Python
1,687
star
6

proxylessnas

[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
C++
1,420
star
7

torchquantum

A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
Jupyter Notebook
1,304
star
8

data-efficient-gans

[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
Python
1,277
star
9

efficientvit

EfficientViT is a new family of vision models for efficient high-resolution vision.
Python
1,218
star
10

torchsparse

[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.
Cuda
1,181
star
11

smoothquant

[ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Python
1,175
star
12

gan-compression

[CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs
Python
1,104
star
13

anycost-gan

[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
Python
778
star
14

tinyml

Python
755
star
15

TinyChatEngine

TinyChatEngine: On-Device LLM Inference Library
C++
730
star
16

tinyengine

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory
C
717
star
17

fastcomposer

[IJCV] FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
Python
644
star
18

pvcnn

[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
Python
639
star
19

lite-transformer

[ICLR 2020] Lite Transformer with Long-Short Range Attention
Python
589
star
20

spvnas

[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Python
577
star
21

distrifuser

[CVPR 2024 Highlight] DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
Python
538
star
22

mcunet

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
Python
460
star
23

tiny-training

On-Device Training Under 256KB Memory [NeurIPS'22]
Python
432
star
24

dlg

[NeurIPS 2019] Deep Leakage From Gradients
Python
400
star
25

haq

[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Python
368
star
26

offsite-tuning

Offsite-Tuning: Transfer Learning without Full Model
Python
365
star
27

hardware-aware-transformers

[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
Python
321
star
28

litepose

[CVPR'22] Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
Python
304
star
29

inter-operator-scheduler

[MLSys 2021] IOS: Inter-Operator Scheduler for CNN Acceleration
C++
191
star
30

amc-models

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Python
166
star
31

apq

[CVPR 2020] APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Python
156
star
32

parallel-computing-tutorial

C++
134
star
33

flatformer

[CVPR'23] FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer
Python
119
star
34

patch_conv

Patch convolution to avoid large GPU memory usage of Conv2D
Python
74
star
35

6s965-fall2022

Jupyter Notebook
64
star
36

sparsevit

[CVPR'23] SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer
Python
48
star
37

bnn-icestick

Binary Neural Network on IceStick FPGA.
Jupyter Notebook
47
star
38

e3d

Efficient 3D Deep Learning
46
star
39

neurips-micronet

[JMLR'20] NeurIPS 2019 MicroNet Challenge Efficient Language Modeling, Champion
Jupyter Notebook
40
star
40

spatten-llm

[HPCA'21] SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
Scala
32
star
41

tinychat-tutorial

C++
28
star
42

pruning-sparsity-publications

14
star
43

iccad-tinyml-open

[ICCAD'22 TinyML Contest] Efficient Heart Stroke Detection on Low-cost Microcontrollers
C
14
star
44

calo-cluster

Jupyter Notebook
5
star
45

ml-blood-pressure

Python
5
star
46

gan-compression-dynamic

Python
3
star
47

data-efficient-gans-dynamic

Python
3
star