• Contents
• ShuffleNetV2 Description
• Model architecture
• Dataset
• Environment Requirements
• Script description
• • Script and sample code
  • Training process
  • • Usage
    • Launch
    • Result
  • Eval process
  • • Usage
    • Launch
    • Result
• Model description
• • Performance
  • • Training Performance
    • Inference Performance
• ModelZoo Homepage

Contents

• ShuffleNetV2 Description

• Model Architecture

• Dataset

• Environment Requirements

• Script Description

  • Script and Sample Code
  • Training Process
  • Evaluation Process
    • Evaluation

• Model Description

  • Performance
    • Training Performance
    • Inference Performance

• ModelZoo Homepage

ShuffleNetV2 Description

ShuffleNetV2 is a much faster and more accurate network than the previous networks on different platforms such as Ascend or GPU.
Paper Ma, N., Zhang, X., Zheng, H. T., & Sun, J. (2018). Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV) (pp. 116-131).

Model architecture

The overall network architecture of ShuffleNetV2 is show below:

Link

Dataset

Dataset used: imagenet

• Dataset size: ~125G, 1.2M colorful images in 1000 classes
  • Train: 120G, 1.2M images
  • Test: 5G, 50000 images
• Data format: RGB images.
  • Note: Data will be processed in src/dataset.py

Environment Requirements

• Hardware(Ascend/GPU)
  • Prepare hardware environment with Ascend or GPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below:
  • MindSpore Tutorials
  • MindSpore Python API

Script description

Script and sample code

+-- ShuffleNetV2
  +-- Readme.md     # descriptions about ShuffleNetV2
  +-- scripts
    +--run_distribute_train_for_ascebd.sh   # shell script for distributed Ascend training
    +--run_distribute_train_for_gpu.sh      # shell script for distributed GPU training
    +--run_eval_for_ascend.sh               # shell script for Ascend evaluation
    +--run_eval_for_gpu.sh                  # shell script for GPU evaluation
    +--run_standalone_train_for_gpu.sh      # shell script for standalone GPU training
  +-- src
    +--config.py                            # parameter configuration
    +--CrossEntropySmooth.py                # loss function for GPU training
    +--dataset.py                           # creating dataset
    +--loss.py                              # loss function for network
    +--lr_generator.py                      # learning rate config
    +--shufflenetv2.py                      # ShuffleNetV2 model network
  +-- train.py                              # training script
  +-- eval.py                               # evaluation script

Training process

Usage

You can start training using python or shell scripts. The usage of shell scripts as follows:

• Distributed training on Ascend: sh run_distribute_train_for_ascend.sh [RANK_TABLE_FILE] [DATASET_PATH]
• Distributed training on GPU: sh run_standalone_train_for_gpu.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
• Standalone training on GPU: sh run_standalone_train_for_gpu.sh [DATASET_PATH]

Launch

# training example
  python:
      GPU: mpirun --allow-run-as-root -n 8 --output-filename log_output --merge-stderr-to-stdout python train.py --is_distributed=True --platform='GPU' --dataset_path='~/imagenet' > train.log 2>&1 &

  shell:
      GPU: cd scripts & sh run_distribute_train_for_gpu.sh 8 0,1,2,3,4,5,6,7 ~/imagenet

Result

Training result will be stored in the example path. Checkpoints will be stored at ./checkpoint by default, and training log will be redirected to ./train/train.log.

Eval process

Usage

You can start evaluation using python or shell scripts. The usage of shell scripts as follows:

• Ascend: sh run_eval_for_ascend.sh [DATASET_PATH] [CHECKPOINT_PATH]
• GPU: sh run_eval_for_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer example
  python:
      Ascend: python eval.py --platform='Ascend' --dataset_path='~/imagenet' --checkpoint='checkpoint_file' > eval.log 2>&1 &
      GPU: CUDA_VISIBLE_DEVICES=0 python eval.py --platform='GPU' --dataset_path='~/imagenet/val/' --checkpoint='checkpoint_file'> eval.log 2>&1 &

  shell:
      Ascend: cd scripts & sh run_eval_for_ascend.sh '~/imagenet' 'checkpoint_file'
      GPU: cd scripts & sh run_eval_for_gpu.sh '~/imagenet' 'checkpoint_file'

checkpoint can be produced in training process.

Result

Inference result will be stored in the example path, you can find result in eval.log.

Model description

Performance

Training Performance

Parameters	Ascend 910	GPU
Model Version	ShuffleNetV2	ShuffleNetV2
Resource	Ascend 910	NV SMX2 V100-32G
uploaded Date	10/09/2021 (month/day/year)	09/24/2020 (month/day/year)
MindSpore Version	1.3.0	1.0.0
Dataset	ImageNet	ImageNet
Training Parameters	src/config.py	src/config.py
Optimizer	Momentum	Momentum
Loss Function	SoftmaxCrossEntropyWithLogits	CrossEntropySmooth
Accuracy	69.59%(TOP1)	69.4%(TOP1)
Total time	11.6 h 8ps	49 h 8ps

Inference Performance

Parameters	Ascend 910	GPU
Resource	Ascend 910	NV SMX2 V100-32G
uploaded Date	10/09/2021 (month/day/year)	09/24/2020 (month/day/year)
MindSpore Version	1.3.0	1.0.0
Dataset	ImageNet	ImageNet
batch_size	125	128
outputs	probability	probability
Accuracy	acc=69.59%(TOP1)	acc=69.4%(TOP1)

ModelZoo Homepage

Please check the official homepage.