mindcv3/configs/shufflenet_v2

• ShuffleNetV2
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment (optional)

ShuffleNetV2

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Introduction

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A key point was raised in ShuffleNetV2, where previous lightweight networks were guided by computing an indirect measure of network complexity, namely FLOPs. The speed of lightweight networks is described by calculating the amount of floating point operations. But the speed of operation was never considered directly. The running speed in mobile devices needs to consider not only FLOPs, but also other factors such as memory accesscost and platform characterics.

Therefore, based on these two principles, ShuffleNetV2 proposes four effective network design principles.

• MAC is minimized when the input feature matrix of the convolutional layer is equal to the output feature matrixchannel (when FLOPs are kept constant).
• MAC increases when the groups of GConv increase (while keeping FLOPs constant).
• the higher the fragmentation of the network design, the slower the speed.
• The impact of Element-wise operation is not negligible.

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
shufflenet_v2_x0_5	D910x8-G	60.68	82.44	1.37	99s/epoch		model	cfg	log
shufflenet_v2_x1_0	D910x8-G	69.51	88.67	2.29	101s/epoch		model	cfg	log
shufflenet_v2_x1_5	D910x8-G	72.59	90.79	3.53	125s/epoch		model	cfg	log
shufflenet_v2_x2_0	D910x8-G	75.14	92.13	7.44	149s/epoch		model	cfg	log

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• Context: GPU_TYPE x pieces - G/F, G - graph mode, F - pynative mode with ms function.

Quick Start

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Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

Please download the ImageNet-1K dataset for model training and validation.

Training

• Hyper-parameters. The hyper-parameter configurations for producing the reported results are stored in the yaml files in mindcv/configs/shufflenetv2 folder. For example, to train with one of these configurations, you can run:

  # train densenet121 on 8 GPUs
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  mpirun -n 8 python train.py -c configs/shufflenetv2/shufflenet_v2_x2_0.yaml --data_dir /path/to/imagenet
  

  Note that the number of GPUs/Ascends and batch size will influence the training results. To reproduce the training result at most, it is recommended to use the same number of GPUs/Ascneds with the same batch size.

• Finetuning. Here is an example for finetuning a pretrained shufflenet_v2_x2_0 on CIFAR10 dataset using Momentum optimizer.

  python train.py --model=shufflenet_v2_x2_0 --pretrained --opt=momentum --lr=0.001 dataset=cifar10 --num_classes=10 --dataset_download
  

Detailed adjustable parameters and their default value can be seen in config.py.

Validation

• To validate the trained model, you can use validate.py. Here is an example for shufflenet_v2_x2_0 to verify the accuracy of pretrained weights.

  python validate.py --model=shufflenet_v2_x2_0 --dataset=imagenet --val_split=val --pretrained
  

• To validate the model, you can use validate.py. Here is an example for shufflenet_v2_x2_0 to verify the accuracy of your training.

  python validate.py --model=shufflenet_v2_x2_0 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/shufflenet_v2_x2_0-best.ckpt'
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.