shufflenetv2

ShuffleNetV2

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Introduction

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.

Figure 1. Architecture Design in ShuffleNetV2 [1]

Requirements

mindspore	ascend driver	firmware	cann toolkit/kernel
2.3.1	24.1.RC2	7.3.0.1.231	8.0.RC2.beta1

Quick Start

Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

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

Training

• Distributed Training

It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run

# distributed training on multiple NPU devices
msrun --bind_core=True --worker_num 8 python train.py --config configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml --data_dir /path/to/imagenet

For detailed illustration of all hyper-parameters, please refer to config.py.

Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.

• Standalone Training

If you want to train or finetune the model on a smaller dataset without distributed training, please run:

# standalone training on single NPU device
python train.py --config configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml --data_dir /path/to/dataset --distribute False

Validation

To validate the accuracy of the trained model, you can use validate.py and parse the checkpoint path with --ckpt_path.

python validate.py -c configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt

Performance

Our reproduced model performance on ImageNet-1K is reported as follows.

Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode.

model name	params(M)	cards	batch size	resolution	jit level	graph compile	ms/step	img/s	acc@top1	acc@top5	recipe	weight
shufflenet_v2_x0_5	1.37	8	64	224x224	O2	100s	47.32	10819.95	60.65	82.26	yaml	weights

Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode.

model name	params(M)	cards	batch size	resolution	jit level	graph compile	ms/step	img/s	acc@top1	acc@top5	recipe	weight
shufflenet_v2_x0_5	1.37	8	64	224x224	O2	62s	41.87	12228.33	60.53	82.11	yaml	weights

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.

References

[1] Ma N, Zhang X, Zheng H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design[C] //Proceedings of the European conference on computer vision (ECCV). 2018: 116-131.